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What field is the article from? | Title: Instant3D: Instant Text-to-3D Generation
Abstract: Text-to-3D generation, which aims to synthesize vivid 3D objects from text
prompts, has attracted much attention from the computer vision community. While
several existing works have achieved impressive results for this task, they
mainly rely on a time-consuming optimization paradigm. Specifically, these
methods optimize a neural field from scratch for each text prompt, taking
approximately one hour or more to generate one object. This heavy and
repetitive training cost impedes their practical deployment. In this paper, we
propose a novel framework for fast text-to-3D generation, dubbed Instant3D.
Once trained, Instant3D is able to create a 3D object for an unseen text prompt
in less than one second with a single run of a feedforward network. We achieve
this remarkable speed by devising a new network that directly constructs a 3D
triplane from a text prompt. The core innovation of our Instant3D lies in our
exploration of strategies to effectively inject text conditions into the
network. Furthermore, we propose a simple yet effective activation function,
the scaled-sigmoid, to replace the original sigmoid function, which speeds up
the training convergence by more than ten times. Finally, to address the Janus
(multi-head) problem in 3D generation, we propose an adaptive Perp-Neg
algorithm that can dynamically adjust its concept negation scales according to
the severity of the Janus problem during training, effectively reducing the
multi-head effect. Extensive experiments on a wide variety of benchmark
datasets demonstrate that the proposed algorithm performs favorably against the
state-of-the-art methods both qualitatively and quantitatively, while achieving
significantly better efficiency. The project page is at
https://ming1993li.github.io/Instant3DProj. | Computer Vision |
What field is the article from? | Title: Two-Stage Classifier for Campaign Negativity Detection using Axis Embeddings: A Case Study on Tweets of Political Users during 2021 Presidential Election in Iran
Abstract: In elections around the world, the candidates may turn their campaigns toward
negativity due to the prospect of failure and time pressure. In the digital
age, social media platforms such as Twitter are rich sources of political
discourse. Therefore, despite the large amount of data that is published on
Twitter, the automatic system for campaign negativity detection can play an
essential role in understanding the strategy of candidates and parties in their
campaigns. In this paper, we propose a hybrid model for detecting campaign
negativity consisting of a two-stage classifier that combines the strengths of
two machine learning models. Here, we have collected Persian tweets from 50
political users, including candidates and government officials. Then we
annotated 5,100 of them that were published during the year before the 2021
presidential election in Iran. In the proposed model, first, the required
datasets of two classifiers based on the cosine similarity of tweet embeddings
with axis embeddings (which are the average of embedding in positive and
negative classes of tweets) from the training set (85\%) are made, and then
these datasets are considered the training set of the two classifiers in the
hybrid model. Finally, our best model (RF-RF) was able to achieve 79\% for the
macro F1 score and 82\% for the weighted F1 score. By running the best model on
the rest of the tweets of 50 political users that were published one year
before the election and with the help of statistical models, we find that the
publication of a tweet by a candidate has nothing to do with the negativity of
that tweet, and the presence of the names of political persons and political
organizations in the tweet is directly related to its negativity. | Machine Learning |
What field is the article from? | Title: SteloCoder: a Decoder-Only LLM for Multi-Language to Python Code Translation
Abstract: With the recent focus on Large Language Models (LLMs), both StarCoder (Li et
al., 2023) and Code Llama (Rozi\`ere et al., 2023) have demonstrated remarkable
performance in code generation. However, there is still a need for improvement
in code translation functionality with efficient training techniques. In
response to this, we introduce SteloCoder, a decoder-only StarCoder-based LLM
designed specifically for multi-programming language-to-Python code
translation. In particular, SteloCoder achieves C++, C#, JavaScript, Java, or
PHP-to-Python code translation without specifying the input programming
language. We modified StarCoder model architecture by incorporating a
Mixture-of-Experts (MoE) technique featuring five experts and a gating network
for multi-task handling. Experts are obtained by StarCoder fine-tuning.
Specifically, we use a Low-Rank Adaptive Method (LoRA) technique, limiting each
expert size as only 0.06% of number of StarCoder's parameters. At the same
time, to enhance training efficiency in terms of time, we adopt curriculum
learning strategy and use self-instruct data for efficient fine-tuning. As a
result, each expert takes only 6 hours to train on one single 80Gb A100 HBM.
With experiments on XLCoST datasets, SteloCoder achieves an average of 73.76
CodeBLEU score in multi-programming language-to-Python translation, surpassing
the top performance from the leaderboard by at least 3.5. This accomplishment
is attributed to only 45M extra parameters with StarCoder as the backbone and
32 hours of valid training on one 80GB A100 HBM. The source code is release
here: https://github.com/sade-adrien/SteloCoder. | Computational Linguistics |
What field is the article from? | Title: Towards Transferable Multi-modal Perception Representation Learning for Autonomy: NeRF-Supervised Masked AutoEncoder
Abstract: This work proposes a unified self-supervised pre-training framework for
transferable multi-modal perception representation learning via masked
multi-modal reconstruction in Neural Radiance Field (NeRF), namely
NeRF-Supervised Masked AutoEncoder (NS-MAE). Specifically, conditioned on
certain view directions and locations, multi-modal embeddings extracted from
corrupted multi-modal input signals, i.e., Lidar point clouds and images, are
rendered into projected multi-modal feature maps via neural rendering. Then,
original multi-modal signals serve as reconstruction targets for the rendered
multi-modal feature maps to enable self-supervised representation learning.
Extensive experiments show that the representation learned via NS-MAE shows
promising transferability for diverse multi-modal and single-modal (camera-only
and Lidar-only) perception models on diverse 3D perception downstream tasks (3D
object detection and BEV map segmentation) with diverse amounts of fine-tuning
labeled data. Moreover, we empirically find that NS-MAE enjoys the synergy of
both the mechanism of masked autoencoder and neural radiance field. We hope
this study can inspire exploration of more general multi-modal representation
learning for autonomous agents. | Computer Vision |
What field is the article from? | Title: Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment
Abstract: Image Quality Assessment (IQA) with reference images have achieved great
success by imitating the human vision system, in which the image quality is
effectively assessed by comparing the query image with its pristine reference
image. However, for the images in the wild, it is quite difficult to access
accurate reference images. We argue that it is possible to learn reference
knowledge under the No-Reference Image Quality Assessment (NR-IQA) setting,
which is effective and efficient empirically. Concretely, by innovatively
introducing a novel feature distillation method in IQA, we propose a new
framework to learn comparative knowledge from non-aligned reference images. And
then, to achieve fast convergence and avoid overfitting, we further propose an
inductive bias regularization. Such a framework not only solves the congenital
defects of NR-IQA but also improves the feature extraction framework, enabling
it to express more abundant quality information. Surprisingly, our method
utilizes less input while obtaining a more significant improvement compared to
the teacher models. Extensive experiments on eight standard NR-IQA datasets
demonstrate the superior performance to the state-of-the-art NR-IQA methods,
i.e., achieving the PLCC values of 0.917 (vs. 0.884 in LIVEC) and 0.686 (vs.
0.661 in LIVEFB). | Computer Vision |
What field is the article from? | Title: Data-Efficient Multimodal Fusion on a Single GPU
Abstract: The goal of multimodal alignment is to learn a single latent space that is
shared between multimodal inputs. The most powerful models in this space have
been trained using massive datasets of paired inputs and large-scale
computational resources, making them prohibitively expensive to train in many
practical scenarios. We surmise that existing unimodal encoders pre-trained on
large amounts of unimodal data should provide an effective bootstrap to create
multimodal models from unimodal ones at much lower costs. We therefore propose
FuseMix, a multimodal augmentation scheme that operates on the latent spaces of
arbitrary pre-trained unimodal encoders. Using FuseMix for multimodal
alignment, we achieve competitive performance -- and in certain cases
outperform state-of-the art methods -- in both image-text and audio-text
retrieval, with orders of magnitude less compute and data: for example, we
outperform CLIP on the Flickr30K text-to-image retrieval task with $\sim \!
600\times$ fewer GPU days and $\sim \! 80\times$ fewer image-text pairs.
Additionally, we show how our method can be applied to convert pre-trained
text-to-image generative models into audio-to-image ones. Code is available at:
https://github.com/layer6ai-labs/fusemix. | Machine Learning |
What field is the article from? | Title: SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data Augmentation
Abstract: While recent two-stage many-to-one deep learning models have demonstrated
great success in 3D human pose estimation, such models are inefficient ways to
detect 3D key points in a sequential video relative to one-shot and
many-to-many models. Another key drawback of two-stage and many-to-one models
is that errors in the first stage will be passed onto the second stage. In this
paper, we introduce SoloPose, a novel one-shot, many-to-many spatio-temporal
transformer model for kinematic 3D human pose estimation of video. SoloPose is
further fortified by HeatPose, a 3D heatmap based on Gaussian Mixture Model
distributions that factors target key points as well as kinematically adjacent
key points. Finally, we address data diversity constraints with the 3D
AugMotion Toolkit, a methodology to augment existing 3D human pose datasets,
specifically by projecting four top public 3D human pose datasets (Humans3.6M,
MADS, AIST Dance++, MPI INF 3DHP) into a novel dataset (Humans7.1M) with a
universal coordinate system. Extensive experiments are conducted on Human3.6M
as well as the augmented Humans7.1M dataset, and SoloPose demonstrates superior
results relative to the state-of-the-art approaches. | Computer Vision |
What field is the article from? | Title: AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
Abstract: With recent advances in multi-modal foundation models, the previously
text-only large language models (LLM) have evolved to incorporate visual input,
opening up unprecedented opportunities for various applications in
visualization. Our work explores the utilization of the visual perception
ability of multi-modal LLMs to develop Autonomous Visualization Agents (AVAs)
that can interpret and accomplish user-defined visualization objectives through
natural language. We propose the first framework for the design of AVAs and
present several usage scenarios intended to demonstrate the general
applicability of the proposed paradigm. The addition of visual perception
allows AVAs to act as the virtual visualization assistant for domain experts
who may lack the knowledge or expertise in fine-tuning visualization outputs.
Our preliminary exploration and proof-of-concept agents suggest that this
approach can be widely applicable whenever the choices of appropriate
visualization parameters require the interpretation of previous visual output.
Feedback from unstructured interviews with experts in AI research, medical
visualization, and radiology has been incorporated, highlighting the
practicality and potential of AVAs. Our study indicates that AVAs represent a
general paradigm for designing intelligent visualization systems that can
achieve high-level visualization goals, which pave the way for developing
expert-level visualization agents in the future. | Human-Computer Interaction |
What field is the article from? | Title: GeoChat: Grounded Large Vision-Language Model for Remote Sensing
Abstract: Recent advancements in Large Vision-Language Models (VLMs) have shown great
promise in natural image domains, allowing users to hold a dialogue about given
visual content. However, such general-domain VLMs perform poorly for Remote
Sensing (RS) scenarios, leading to inaccurate or fabricated information when
presented with RS domain-specific queries. Such a behavior emerges due to the
unique challenges introduced by RS imagery. For example, to handle
high-resolution RS imagery with diverse scale changes across categories and
many small objects, region-level reasoning is necessary alongside holistic
scene interpretation. Furthermore, the lack of domain-specific multimodal
instruction following data as well as strong backbone models for RS make it
hard for the models to align their behavior with user queries. To address these
limitations, we propose GeoChat - the first versatile remote sensing VLM that
offers multitask conversational capabilities with high-resolution RS images.
Specifically, GeoChat can not only answer image-level queries but also accepts
region inputs to hold region-specific dialogue. Furthermore, it can visually
ground objects in its responses by referring to their spatial coordinates. To
address the lack of domain-specific datasets, we generate a novel RS multimodal
instruction-following dataset by extending image-text pairs from existing
diverse RS datasets. We establish a comprehensive benchmark for RS multitask
conversations and compare with a number of baseline methods. GeoChat
demonstrates robust zero-shot performance on various RS tasks, e.g., image and
region captioning, visual question answering, scene classification, visually
grounded conversations and referring detection. Our code is available at
https://github.com/mbzuai-oryx/geochat. | Computer Vision |
What field is the article from? | Title: Preserving Patient Privacy in MRI Scans: A Comprehensive Approach with 3D Masked Autoencoders
Abstract: MRI scans provide valuable medical information, however they also contain
sensitive and personally identifiable information (PII) that needs to be
protected. Whereas MRI metadata is easily sanitized, MRI image data is a
privacy risk because it contains information to render highly-realistic 3D
visualizations of a patient's head, enabling malicious actors to possibly
identify the subject by cross-referencing a database. Data anonymization and
de-identification is concerned with ensuring the privacy and confidentiality of
individuals' personal information. Traditional MRI de-identification methods
remove privacy-sensitive parts (e.g. eyes, nose etc.) from a given scan. This
comes at the expense of introducing a domain shift that can throw off
downstream analyses. Recently, a GAN-based approach was proposed to de-identify
a patient's scan by remodeling it (\eg changing the face) rather than by
removing parts. In this work, we propose CP-MAE, a model that de-identifies the
face using masked autoencoders and that outperforms all previous approaches in
terms of downstream task performance as well as de-identification. With our
method we are able to synthesize scans of resolution up to $256^3$ (previously
$128^3$) which constitutes an eight-fold increase in the number of voxels.
Using our construction we were able to design a system that exhibits a highly
robust training stage, making it easy to fit the network on novel data. | Computer Vision |
What field is the article from? | Title: Intelligent Virtual Assistants with LLM-based Process Automation
Abstract: While intelligent virtual assistants like Siri, Alexa, and Google Assistant
have become ubiquitous in modern life, they still face limitations in their
ability to follow multi-step instructions and accomplish complex goals
articulated in natural language. However, recent breakthroughs in large
language models (LLMs) show promise for overcoming existing barriers by
enhancing natural language processing and reasoning capabilities. Though
promising, applying LLMs to create more advanced virtual assistants still faces
challenges like ensuring robust performance and handling variability in
real-world user commands. This paper proposes a novel LLM-based virtual
assistant that can automatically perform multi-step operations within mobile
apps based on high-level user requests. The system represents an advance in
assistants by providing an end-to-end solution for parsing instructions,
reasoning about goals, and executing actions. LLM-based Process Automation
(LLMPA) has modules for decomposing instructions, generating descriptions,
detecting interface elements, predicting next actions, and error checking.
Experiments demonstrate the system completing complex mobile operation tasks in
Alipay based on natural language instructions. This showcases how large
language models can enable automated assistants to accomplish real-world tasks.
The main contributions are the novel LLMPA architecture optimized for app
process automation, the methodology for applying LLMs to mobile apps, and
demonstrations of multi-step task completion in a real-world environment.
Notably, this work represents the first real-world deployment and extensive
evaluation of a large language model-based virtual assistant in a widely used
mobile application with an enormous user base numbering in the hundreds of
millions. | Machine Learning |
What field is the article from? | Title: Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMs
Abstract: Multimodal large language models (MLLMs) have shown remarkable capabilities
across a broad range of tasks but their knowledge and abilities in the
geographic and geospatial domains are yet to be explored, despite potential
wide-ranging benefits to navigation, environmental research, urban development,
and disaster response. We conduct a series of experiments exploring various
vision capabilities of MLLMs within these domains, particularly focusing on the
frontier model GPT-4V, and benchmark its performance against open-source
counterparts. Our methodology involves challenging these models with a
small-scale geographic benchmark consisting of a suite of visual tasks, testing
their abilities across a spectrum of complexity. The analysis uncovers not only
where such models excel, including instances where they outperform humans, but
also where they falter, providing a balanced view of their capabilities in the
geographic domain. To enable the comparison and evaluation of future models,
our benchmark will be publicly released. | Computer Vision |
What field is the article from? | Title: Interpretable Neural PDE Solvers using Symbolic Frameworks
Abstract: Partial differential equations (PDEs) are ubiquitous in the world around us,
modelling phenomena from heat and sound to quantum systems. Recent advances in
deep learning have resulted in the development of powerful neural solvers;
however, while these methods have demonstrated state-of-the-art performance in
both accuracy and computational efficiency, a significant challenge remains in
their interpretability. Most existing methodologies prioritize predictive
accuracy over clarity in the underlying mechanisms driving the model's
decisions. Interpretability is crucial for trustworthiness and broader
applicability, especially in scientific and engineering domains where neural
PDE solvers might see the most impact. In this context, a notable gap in
current research is the integration of symbolic frameworks (such as symbolic
regression) into these solvers. Symbolic frameworks have the potential to
distill complex neural operations into human-readable mathematical expressions,
bridging the divide between black-box predictions and solutions. | Artificial Intelligence |
What field is the article from? | Title: Score Models for Offline Goal-Conditioned Reinforcement Learning
Abstract: Offline Goal-Conditioned Reinforcement Learning (GCRL) is tasked with
learning to achieve multiple goals in an environment purely from offline
datasets using sparse reward functions. Offline GCRL is pivotal for developing
generalist agents capable of leveraging pre-existing datasets to learn diverse
and reusable skills without hand-engineering reward functions. However,
contemporary approaches to GCRL based on supervised learning and contrastive
learning are often suboptimal in the offline setting. An alternative
perspective on GCRL optimizes for occupancy matching, but necessitates learning
a discriminator, which subsequently serves as a pseudo-reward for downstream
RL. Inaccuracies in the learned discriminator can cascade, negatively
influencing the resulting policy. We present a novel approach to GCRL under a
new lens of mixture-distribution matching, leading to our discriminator-free
method: SMORe. The key insight is combining the occupancy matching perspective
of GCRL with a convex dual formulation to derive a learning objective that can
better leverage suboptimal offline data. SMORe learns scores or unnormalized
densities representing the importance of taking an action at a state for
reaching a particular goal. SMORe is principled and our extensive experiments
on the fully offline GCRL benchmark composed of robot manipulation and
locomotion tasks, including high-dimensional observations, show that SMORe can
outperform state-of-the-art baselines by a significant margin. | Machine Learning |
What field is the article from? | Title: Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
Abstract: While reinforcement learning has achieved remarkable successes in several
domains, its real-world application is limited due to many methods failing to
generalise to unfamiliar conditions. In this work, we consider the problem of
generalising to new transition dynamics, corresponding to cases in which the
environment's response to the agent's actions differs. For example, the
gravitational force exerted on a robot depends on its mass and changes the
robot's mobility. Consequently, in such cases, it is necessary to condition an
agent's actions on extrinsic state information and pertinent contextual
information reflecting how the environment responds. While the need for
context-sensitive policies has been established, the manner in which context is
incorporated architecturally has received less attention. Thus, in this work,
we present an investigation into how context information should be incorporated
into behaviour learning to improve generalisation. To this end, we introduce a
neural network architecture, the Decision Adapter, which generates the weights
of an adapter module and conditions the behaviour of an agent on the context
information. We show that the Decision Adapter is a useful generalisation of a
previously proposed architecture and empirically demonstrate that it results in
superior generalisation performance compared to previous approaches in several
environments. Beyond this, the Decision Adapter is more robust to irrelevant
distractor variables than several alternative methods. | Artificial Intelligence |
What field is the article from? | Title: An Intelligent Social Learning-based Optimization Strategy for Black-box Robotic Control with Reinforcement Learning
Abstract: Implementing intelligent control of robots is a difficult task, especially
when dealing with complex black-box systems, because of the lack of visibility
and understanding of how these robots work internally. This paper proposes an
Intelligent Social Learning (ISL) algorithm to enable intelligent control of
black-box robotic systems. Inspired by mutual learning among individuals in
human social groups, ISL includes learning, imitation, and self-study styles.
Individuals in the learning style use the Levy flight search strategy to learn
from the best performer and form the closest relationships. In the imitation
style, individuals mimic the best performer with a second-level rapport by
employing a random perturbation strategy. In the self-study style, individuals
learn independently using a normal distribution sampling method while
maintaining a distant relationship with the best performer. Individuals in the
population are regarded as autonomous intelligent agents in each style. Neural
networks perform strategic actions in three styles to interact with the
environment and the robot and iteratively optimize the network policy. Overall,
ISL builds on the principles of intelligent optimization, incorporating ideas
from reinforcement learning, and possesses strong search capabilities, fast
computation speed, fewer hyperparameters, and insensitivity to sparse rewards.
The proposed ISL algorithm is compared with four state-of-the-art methods on
six continuous control benchmark cases in MuJoCo to verify its effectiveness
and advantages. Furthermore, ISL is adopted in the simulation and experimental
grasping tasks of the UR3 robot for validations, and satisfactory solutions are
yielded. | Artificial Intelligence |
What field is the article from? | Title: Legal-HNet: Mixing Legal Long-Context Tokens with Hartley Transform
Abstract: Since its introduction, the transformers architecture has seen great adoption
in NLP applications, but it also has limitations. Although the self-attention
mechanism allows for generating very rich representations of the input text,
its effectiveness may be limited in specialized domains such as legal, where,
for example, language models often have to process very long texts. In this
paper, we explore alternatives to replace the attention-based layers with
simpler token-mixing mechanisms: Hartley and Fourier transforms. Using these
non-parametric techniques, we train models with long input documents from
scratch in the legal domain setting. We also introduce a new hybrid Seq2Seq
architecture, a no-attention-based encoder connected with an attention-based
decoder, which performs quite well on existing summarization tasks with much
less compute and memory requirements. We believe that similar, if not better
performance, as in the case of long correlations of abstractive text
summarization tasks, can be achieved by adopting these simpler infrastructures.
This not only makes training models from scratch accessible to more people, but
also contributes to the reduction of the carbon footprint during training. | Computational Linguistics |
What field is the article from? | Title: Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging
Abstract: Artificial Intelligence (AI) models are vulnerable to information leakage of
their training data, which can be highly sensitive, for example in medical
imaging. Privacy Enhancing Technologies (PETs), such as Differential Privacy
(DP), aim to circumvent these susceptibilities. DP is the strongest possible
protection for training models while bounding the risks of inferring the
inclusion of training samples or reconstructing the original data. DP achieves
this by setting a quantifiable privacy budget. Although a lower budget
decreases the risk of information leakage, it typically also reduces the
performance of such models. This imposes a trade-off between robust performance
and stringent privacy. Additionally, the interpretation of a privacy budget
remains abstract and challenging to contextualize. In this study, we contrast
the performance of AI models at various privacy budgets against both,
theoretical risk bounds and empirical success of reconstruction attacks. We
show that using very large privacy budgets can render reconstruction attacks
impossible, while drops in performance are negligible. We thus conclude that
not using DP -- at all -- is negligent when applying AI models to sensitive
data. We deem those results to lie a foundation for further debates on striking
a balance between privacy risks and model performance. | Cryptography and Security |
What field is the article from? | Title: Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach
Abstract: The significant advancements in large language models (LLMs) have presented
novel opportunities for tackling planning and decision-making within
multi-agent systems. However, as the number of agents increases, the issues of
hallucination in LLMs and coordination in multi-agent systems (MAS) have become
increasingly pronounced. Additionally, the efficient utilization of tokens
becomes a critical consideration when employing LLMs to facilitate the
interactions of large numbers of agents. In this paper, we present a novel
framework aimed at enhancing coordination and decision-making capabilities of
LLMs within large-scale multi-agent environments. Our approach draws
inspiration from the actor-critic framework employed in multi-agent
reinforcement learning, and we develop a modular and token-efficient solution
that effectively addresses challenges presented by LLMs and MAS. Through
evaluations conducted in experiments involving system resource allocation and
robot grid transportation, we demonstrate the considerable advantages afforded
by our proposed approach. | Artificial Intelligence |
What field is the article from? | Title: Adversarial Learning for Feature Shift Detection and Correction
Abstract: Data shift is a phenomenon present in many real-world applications, and while
there are multiple methods attempting to detect shifts, the task of localizing
and correcting the features originating such shifts has not been studied in
depth. Feature shifts can occur in many datasets, including in multi-sensor
data, where some sensors are malfunctioning, or in tabular and structured data,
including biomedical, financial, and survey data, where faulty standardization
and data processing pipelines can lead to erroneous features. In this work, we
explore using the principles of adversarial learning, where the information
from several discriminators trained to distinguish between two distributions is
used to both detect the corrupted features and fix them in order to remove the
distribution shift between datasets. We show that mainstream supervised
classifiers, such as random forest or gradient boosting trees, combined with
simple iterative heuristics, can localize and correct feature shifts,
outperforming current statistical and neural network-based techniques. The code
is available at https://github.com/AI-sandbox/DataFix. | Machine Learning |
What field is the article from? | Title: Redefining the Laparoscopic Spatial Sense: AI-based Intra- and Postoperative Measurement from Stereoimages
Abstract: A significant challenge in image-guided surgery is the accurate measurement
task of relevant structures such as vessel segments, resection margins, or
bowel lengths. While this task is an essential component of many surgeries, it
involves substantial human effort and is prone to inaccuracies. In this paper,
we develop a novel human-AI-based method for laparoscopic measurements
utilizing stereo vision that has been guided by practicing surgeons. Based on a
holistic qualitative requirements analysis, this work proposes a comprehensive
measurement method, which comprises state-of-the-art machine learning
architectures, such as RAFT-Stereo and YOLOv8. The developed method is assessed
in various realistic experimental evaluation environments. Our results outline
the potential of our method achieving high accuracies in distance measurements
with errors below 1 mm. Furthermore, on-surface measurements demonstrate
robustness when applied in challenging environments with textureless regions.
Overall, by addressing the inherent challenges of image-guided surgery, we lay
the foundation for a more robust and accurate solution for intra- and
postoperative measurements, enabling more precise, safe, and efficient surgical
procedures. | Computer Vision |
What field is the article from? | Title: Calibrated Language Models Must Hallucinate
Abstract: Recent language models generate false but plausible-sounding text with
surprising frequency. Such "hallucinations" are an obstacle to the usability of
language-based AI systems and can harm people who rely upon their outputs. This
work shows shows that there is an inherent statistical lower-bound on the rate
that pretrained language models hallucinate certain types of facts, having
nothing to do with the transformer LM architecture or data quality. For
"arbitrary" facts whose veracity cannot be determined from the training data,
we show that hallucinations must occur at a certain rate for language models
that satisfy a statistical calibration condition appropriate for generative
language models. Specifically, if the maximum probability of any fact is
bounded, we show that the probability of generating a hallucination is close to
the fraction of facts that occur exactly once in the training data (a
"Good-Turing" estimate), even assuming ideal training data without errors.
One conclusion is that models pretrained to be sufficiently good predictors
(i.e., calibrated) may require post-training to mitigate hallucinations on the
type of arbitrary facts that tend to appear once in the training set. However,
our analysis also suggests that there is no statistical reason that pretraining
will lead to hallucination on facts that tend to appear more than once in the
training data (like references to publications such as articles and books,
whose hallucinations have been particularly notable and problematic) or on
systematic facts (like arithmetic calculations). Therefore, different
architectures and learning algorithms may mitigate these latter types of
hallucinations. | Computational Linguistics |
What field is the article from? | Title: HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models
Abstract: Theory of Mind (ToM) is the ability to reason about one's own and others'
mental states. ToM plays a critical role in the development of intelligence,
language understanding, and cognitive processes. While previous work has
primarily focused on first and second-order ToM, we explore higher-order ToM,
which involves recursive reasoning on others' beliefs. We introduce HI-TOM, a
Higher Order Theory of Mind benchmark. Our experimental evaluation using
various Large Language Models (LLMs) indicates a decline in performance on
higher-order ToM tasks, demonstrating the limitations of current LLMs. We
conduct a thorough analysis of different failure cases of LLMs, and share our
thoughts on the implications of our findings on the future of NLP. | Computational Linguistics |
What field is the article from? | Title: A Contrastive Compositional Benchmark for Text-to-Image Synthesis: A Study with Unified Text-to-Image Fidelity Metrics
Abstract: Text-to-image (T2I) synthesis has recently achieved significant advancements.
However, challenges remain in the model's compositionality, which is the
ability to create new combinations from known components. We introduce
Winoground-T2I, a benchmark designed to evaluate the compositionality of T2I
models. This benchmark includes 11K complex, high-quality contrastive sentence
pairs spanning 20 categories. These contrastive sentence pairs with subtle
differences enable fine-grained evaluations of T2I synthesis models.
Additionally, to address the inconsistency across different metrics, we propose
a strategy that evaluates the reliability of various metrics by using
comparative sentence pairs. We use Winoground-T2I with a dual objective: to
evaluate the performance of T2I models and the metrics used for their
evaluation. Finally, we provide insights into the strengths and weaknesses of
these metrics and the capabilities of current T2I models in tackling challenges
across a range of complex compositional categories. Our benchmark is publicly
available at https://github.com/zhuxiangru/Winoground-T2I . | Computer Vision |
What field is the article from? | Title: Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images
Abstract: Distribution shifts are characterized by differences between the training and
test data distributions. They can significantly reduce the accuracy of machine
learning models deployed in real-world scenarios. This paper explores the
distribution shift problem when classifying pollen grains from microscopic
images collected in the wild with a low-cost camera sensor. We leverage the
domain knowledge that geometric features are highly important for accurate
pollen identification and introduce two novel geometric image augmentation
techniques to significantly narrow the accuracy gap between the model
performance on the train and test datasets. In particular, we show that
Tenengrad and ImageToSketch filters are highly effective to balance the shape
and texture information while leaving out unimportant details that may confuse
the model. Extensive evaluations on various model architectures demonstrate a
consistent improvement of the model generalization to field data of up to 14%
achieved by the geometric augmentation techniques when compared to a wide range
of standard image augmentations. The approach is validated through an ablation
study using pollen hydration tests to recover the shape of dry pollen grains.
The proposed geometric augmentations also receive the highest scores according
to the affinity and diversity measures from the literature. | Computer Vision |
What field is the article from? | Title: An Efficient Self-Supervised Cross-View Training For Sentence Embedding
Abstract: Self-supervised sentence representation learning is the task of constructing
an embedding space for sentences without relying on human annotation efforts.
One straightforward approach is to finetune a pretrained language model (PLM)
with a representation learning method such as contrastive learning. While this
approach achieves impressive performance on larger PLMs, the performance
rapidly degrades as the number of parameters decreases. In this paper, we
propose a framework called Self-supervised Cross-View Training (SCT) to narrow
the performance gap between large and small PLMs. To evaluate the effectiveness
of SCT, we compare it to 5 baseline and state-of-the-art competitors on seven
Semantic Textual Similarity (STS) benchmarks using 5 PLMs with the number of
parameters ranging from 4M to 340M. The experimental results show that STC
outperforms the competitors for PLMs with less than 100M parameters in 18 of 21
cases. | Computational Linguistics |
What field is the article from? | Title: Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?
Abstract: The research field of end-user programming has largely been concerned with
helping non-experts learn to code sufficiently well in order to achieve their
tasks. Generative AI stands to obviate this entirely by allowing users to
generate code from naturalistic language prompts. In this essay, we explore the
extent to which "traditional" programming languages remain relevant for
non-expert end-user programmers in a world with generative AI. We posit the
"generative shift hypothesis": that generative AI will create qualitative and
quantitative expansions in the traditional scope of end-user programming. We
outline some reasons that traditional programming languages may still be
relevant and useful for end-user programmers. We speculate whether each of
these reasons might be fundamental and enduring, or whether they may disappear
with further improvements and innovations in generative AI. Finally, we
articulate a set of implications for end-user programming research, including
the possibility of needing to revisit many well-established core concepts, such
as Ko's learning barriers and Blackwell's attention investment model. | Human-Computer Interaction |
What field is the article from? | Title: ALYMPICS: Language Agents Meet Game Theory
Abstract: This paper introduces Alympics, a platform that leverages Large Language
Model (LLM) agents to facilitate investigations in game theory. By employing
LLMs and autonomous agents to simulate human behavior and enable multi-agent
collaborations, we can construct realistic and dynamic models of human
interactions for game theory hypothesis formulating and testing. To demonstrate
this, we present and implement a survival game involving unequal competition
for limited resources. Through manipulation of resource availability and agent
personalities, we observe how different agents engage in the competition and
adapt their strategies. The use of LLM agents in game theory research offers
significant advantages, including simulating realistic behavior, providing a
controlled, scalable, and reproducible environment. Our work highlights the
potential of LLM agents in enhancing the understanding of strategic
decision-making within complex socioeconomic contexts. All codes are available
at https://github.com/microsoft/Alympics | Computational Linguistics |
What field is the article from? | Title: MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following
Abstract: In the realm of large language models (LLMs), enhancing instruction-following
capability often involves curating expansive training data. This is achieved
through two primary schemes: i) Scaling-Inputs: Amplifying (input, output)
pairs per task instruction, aiming for better instruction adherence. ii)
Scaling Input-Free Tasks: Enlarging tasks, each composed of an (instruction,
output) pair (without requiring a separate input anymore). However, LLMs under
Scaling-Inputs tend to be overly sensitive to inputs, leading to
misinterpretation or non-compliance with instructions. Conversely, Scaling
Input-Free Tasks demands a substantial number of tasks but is less effective in
instruction following when dealing with instances in Scaling-Inputs. This work
introduces MUFFIN, a new scheme of instruction-following dataset curation.
Specifically, we automatically Scale Tasks per Input by diversifying these
tasks with various input facets. Experimental results across four zero-shot
benchmarks, spanning both Scaling-Inputs and Scaling Input-Free Tasks schemes,
reveal that LLMs, at various scales, trained on MUFFIN generally demonstrate
superior instruction-following capabilities compared to those trained on the
two aforementioned schemes. | Computational Linguistics |
What field is the article from? | Title: Retrieving Conditions from Reference Images for Diffusion Models
Abstract: Recent diffusion-based subject driven generative methods have enabled image
generations with good fidelity for specific objects or human portraits.
However, to achieve better versatility for applications, we argue that not only
improved datasets and evaluations are desired, but also more careful methods to
retrieve only relevant information from conditional images are anticipated. To
this end, we propose an anime figures dataset RetriBooru-V1, with enhanced
identity and clothing labels. We state new tasks enabled by this dataset, and
introduce a new diversity metric to measure success in completing these tasks,
quantifying the flexibility of image generations. We establish an RAG-inspired
baseline method, designed to retrieve precise conditional information from
reference images. Then, we compare with current methods on existing task to
demonstrate the capability of the proposed method. Finally, we provide baseline
experiment results on new tasks, and conduct ablation studies on the possible
structural choices. | Computer Vision |
What field is the article from? | Title: SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction
Abstract: 3D occupancy prediction is an important task for the robustness of
vision-centric autonomous driving, which aims to predict whether each point is
occupied in the surrounding 3D space. Existing methods usually require 3D
occupancy labels to produce meaningful results. However, it is very laborious
to annotate the occupancy status of each voxel. In this paper, we propose
SelfOcc to explore a self-supervised way to learn 3D occupancy using only video
sequences. We first transform the images into the 3D space (e.g., bird's eye
view) to obtain 3D representation of the scene. We directly impose constraints
on the 3D representations by treating them as signed distance fields. We can
then render 2D images of previous and future frames as self-supervision signals
to learn the 3D representations. We propose an MVS-embedded strategy to
directly optimize the SDF-induced weights with multiple depth proposals. Our
SelfOcc outperforms the previous best method SceneRF by 58.7% using a single
frame as input on SemanticKITTI and is the first self-supervised work that
produces reasonable 3D occupancy for surround cameras on nuScenes. SelfOcc
produces high-quality depth and achieves state-of-the-art results on novel
depth synthesis, monocular depth estimation, and surround-view depth estimation
on the SemanticKITTI, KITTI-2015, and nuScenes, respectively. Code:
https://github.com/huang-yh/SelfOcc. | Computer Vision |
What field is the article from? | Title: A Local Appearance Model for Volumetric Capture of Diverse Hairstyle
Abstract: Hair plays a significant role in personal identity and appearance, making it
an essential component of high-quality, photorealistic avatars. Existing
approaches either focus on modeling the facial region only or rely on
personalized models, limiting their generalizability and scalability. In this
paper, we present a novel method for creating high-fidelity avatars with
diverse hairstyles. Our method leverages the local similarity across different
hairstyles and learns a universal hair appearance prior from multi-view
captures of hundreds of people. This prior model takes 3D-aligned features as
input and generates dense radiance fields conditioned on a sparse point cloud
with color. As our model splits different hairstyles into local primitives and
builds prior at that level, it is capable of handling various hair topologies.
Through experiments, we demonstrate that our model captures a diverse range of
hairstyles and generalizes well to challenging new hairstyles. Empirical
results show that our method improves the state-of-the-art approaches in
capturing and generating photorealistic, personalized avatars with complete
hair. | Computer Vision |
What field is the article from? | Title: Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion
Abstract: Deep reinforcement learning (RL) can enable robots to autonomously acquire
complex behaviors, such as legged locomotion. However, RL in the real world is
complicated by constraints on efficiency, safety, and overall training
stability, which limits its practical applicability. We present APRL, a policy
regularization framework that modulates the robot's exploration over the course
of training, striking a balance between flexible improvement potential and
focused, efficient exploration. APRL enables a quadrupedal robot to efficiently
learn to walk entirely in the real world within minutes and continue to improve
with more training where prior work saturates in performance. We demonstrate
that continued training with APRL results in a policy that is substantially
more capable of navigating challenging situations and is able to adapt to
changes in dynamics with continued training. | Robotics |
What field is the article from? | Title: NeuroFlow: Development of lightweight and efficient model integration scheduling strategy for autonomous driving system
Abstract: This paper proposes a specialized autonomous driving system that takes into
account the unique constraints and characteristics of automotive systems,
aiming for innovative advancements in autonomous driving technology. The
proposed system systematically analyzes the intricate data flow in autonomous
driving and provides functionality to dynamically adjust various factors that
influence deep learning models. Additionally, for algorithms that do not rely
on deep learning models, the system analyzes the flow to determine resource
allocation priorities. In essence, the system optimizes data flow and schedules
efficiently to ensure real-time performance and safety. The proposed system was
implemented in actual autonomous vehicles and experimentally validated across
various driving scenarios. The experimental results provide evidence of the
system's stable inference and effective control of autonomous vehicles, marking
a significant turning point in the development of autonomous driving systems. | Robotics |
What field is the article from? | Title: Modyn: A Platform for Model Training on Dynamic Datasets With Sample-Level Data Selection
Abstract: Machine learning training data is often dynamic in real-world use cases,
i.e., data is added or removed and may experience distribution shifts over
time. Models must incorporate this evolving training data to improve
generalization, adapt to potential distribution shifts, and adhere to privacy
regulations. However, the cost of model (re)training is proportional to how
often the model trains and on how much data it trains on. While ML research
explores these topics in isolation, there is no end-to-end open-source platform
to facilitate the exploration of model retraining and data selection policies
and the deployment these algorithms efficiently at scale.
We present Modyn, a platform for model training on dynamic datasets that
enables sample-level data selection and triggering policies. Modyn orchestrates
continuous training pipelines while optimizing the underlying system
infrastructure to support fast access to arbitrary data samples for efficient
data selection. Modyn's extensible architecture allows users to run training
pipelines without modifying the platform code, and enables researchers to
effortlessly extend the system. We evaluate Modyn's training throughput,
showing that even in memory-bound recommendation systems workloads, Modyn is
able to reach 80 to 100 % of the throughput compared to loading big chunks of
data locally without sample-level data selection. Additionally, we showcase
Modyn's functionality with three different data selection policies. | Machine Learning |
What field is the article from? | Title: Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
Abstract: Text embedding models have emerged as powerful tools for transforming
sentences into fixed-sized feature vectors that encapsulate semantic
information. While these models are essential for tasks like information
retrieval, semantic clustering, and text re-ranking, most existing open-source
models, especially those built on architectures like BERT, struggle to
represent lengthy documents and often resort to truncation. One common approach
to mitigate this challenge involves splitting documents into smaller paragraphs
for embedding. However, this strategy results in a much larger set of vectors,
consequently leading to increased memory consumption and computationally
intensive vector searches with elevated latency.
To address these challenges, we introduce Jina Embeddings 2, an open-source
text embedding model capable of accommodating up to 8192 tokens. This model is
designed to transcend the conventional 512-token limit and adeptly process long
documents. Jina Embeddings 2 not only achieves state-of-the-art performance on
a range of embedding-related tasks in the MTEB benchmark but also matches the
performance of OpenAI's proprietary ada-002 model. Additionally, our
experiments indicate that an extended context can enhance performance in tasks
such as NarrativeQA. | Computational Linguistics |
What field is the article from? | Title: Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?
Abstract: This paper presents a pipeline to detect and explain anomalous reviews in
online platforms. The pipeline is made up of three modules and allows the
detection of reviews that do not generate value for users due to either
worthless or malicious composition. The classifications are accompanied by a
normality score and an explanation that justifies the decision made. The
pipeline's ability to solve the anomaly detection task was evaluated using
different datasets created from a large Amazon database. Additionally, a study
comparing three explainability techniques involving 241 participants was
conducted to assess the explainability module. The study aimed to measure the
impact of explanations on the respondents' ability to reproduce the
classification model and their perceived usefulness. This work can be useful to
automate tasks in review online platforms, such as those for electronic
commerce, and offers inspiration for addressing similar problems in the field
of anomaly detection in textual data. We also consider it interesting to have
carried out a human evaluation of the capacity of different explainability
techniques in a real and infrequent scenario such as the detection of anomalous
reviews, as well as to reflect on whether it is possible to explain tasks as
humanly subjective as this one. | Computational Linguistics |
What field is the article from? | Title: InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions
Abstract: In collaborative human-robot manipulation, a robot must predict human intents
and adapt its actions accordingly to smoothly execute tasks. However, the
human's intent in turn depends on actions the robot takes, creating a
chicken-or-egg problem. Prior methods ignore such inter-dependency and instead
train marginal intent prediction models independent of robot actions. This is
because training conditional models is hard given a lack of paired human-robot
interaction datasets.
Can we instead leverage large-scale human-human interaction data that is more
easily accessible? Our key insight is to exploit a correspondence between human
and robot actions that enables transfer learning from human-human to
human-robot data. We propose a novel architecture, InteRACT, that pre-trains a
conditional intent prediction model on large human-human datasets and
fine-tunes on a small human-robot dataset. We evaluate on a set of real-world
collaborative human-robot manipulation tasks and show that our conditional
model improves over various marginal baselines. We also introduce new
techniques to tele-operate a 7-DoF robot arm and collect a diverse range of
human-robot collaborative manipulation data, which we open-source. | Robotics |
What field is the article from? | Title: Class-Discriminative Attention Maps for Vision Transformers
Abstract: Interpretability methods are critical components for examining and exploring
deep neural networks (DNN), as well as increasing our understanding of and
trust in them. Vision transformers (ViT), which can be trained to
state-of-the-art performance with a self-supervised learning (SSL) training
method, provide built-in attention maps (AM). While AMs can provide
high-quality semantic segmentation of input images, they do not account for any
signal coming from a downstream classifier. We introduce class-discriminative
attention maps (CDAM), a novel post-hoc explanation method that is highly
sensitive to the target class. Our method essentially scales attention scores
by how relevant the corresponding tokens are for the predictions of a
classifier head. Alternative to classifier outputs, CDAM can also explain a
user-defined concept by targeting similarity measures in the latent space of
the ViT. This allows for explanations of arbitrary concepts, defined by the
user through a few sample images. We investigate the operating characteristics
of CDAM in comparison with relevance propagation (RP) and token ablation maps
(TAM), an alternative to pixel occlusion methods. CDAM is highly
class-discriminative and semantically relevant, while providing implicit
regularization of relevance scores.
PyTorch implementation: \url{https://github.com/lenbrocki/CDAM}
Web live demo: \url{https://cdam.informatism.com/} | Computer Vision |
What field is the article from? | Title: KnowGPT: Black-Box Knowledge Injection for Large Language Models
Abstract: Generative Large Language Models (LLMs), such as ChatGPT, offer interactive
APIs that can answer common questions at a human-expert level. However, these
models often give inaccurate or incorrect responses when faced with questions
requiring domain-specific or professional-specific knowledge not covered in
their training corpus. Furthermore, many state-of-the-art LLMs are not
open-source, making it challenging to inject knowledge with model APIs only. In
this work, we introduce KnowGPT, a black-box knowledge injection framework for
LLMs in question answering. KnowGPT leverages deep reinforcement learning (RL)
to extract relevant knowledge from Knowledge Graphs (KGs) and use Multi-Armed
Bandit (MAB) to construct the most suitable prompt for each question. Our
extensive experiments on three benchmark datasets showcase that KnowGPT
significantly enhances the existing methods. Notably, KnowGPT achieves an
average improvement of 23.7% over ChatGPT and an average improvement of 2.9%
over GPT-4. Additionally, KnowGPT attains a 91.6% accuracy on the OpenbookQA
official leaderboard, which is comparable to human-level performance. | Computational Linguistics |
What field is the article from? | Title: Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies
Abstract: Thematic analysis and other variants of inductive coding are widely used
qualitative analytic methods within empirical legal studies (ELS). We propose a
novel framework facilitating effective collaboration of a legal expert with a
large language model (LLM) for generating initial codes (phase 2 of thematic
analysis), searching for themes (phase 3), and classifying the data in terms of
the themes (to kick-start phase 4). We employed the framework for an analysis
of a dataset (n=785) of facts descriptions from criminal court opinions
regarding thefts. The goal of the analysis was to discover classes of typical
thefts. Our results show that the LLM, namely OpenAI's GPT-4, generated
reasonable initial codes, and it was capable of improving the quality of the
codes based on expert feedback. They also suggest that the model performed well
in zero-shot classification of facts descriptions in terms of the themes.
Finally, the themes autonomously discovered by the LLM appear to map fairly
well to the themes arrived at by legal experts. These findings can be leveraged
by legal researchers to guide their decisions in integrating LLMs into their
thematic analyses, as well as other inductive coding projects. | Artificial Intelligence |
What field is the article from? | Title: Optimal Cost Constrained Adversarial Attacks For Multiple Agent Systems
Abstract: Finding optimal adversarial attack strategies is an important topic in
reinforcement learning and the Markov decision process. Previous studies
usually assume one all-knowing coordinator (attacker) for whom attacking
different recipient (victim) agents incurs uniform costs. However, in reality,
instead of using one limitless central attacker, the attacks often need to be
performed by distributed attack agents. We formulate the problem of performing
optimal adversarial agent-to-agent attacks using distributed attack agents, in
which we impose distinct cost constraints on each different attacker-victim
pair. We propose an optimal method integrating within-step static constrained
attack-resource allocation optimization and between-step dynamic programming to
achieve the optimal adversarial attack in a multi-agent system. Our numerical
results show that the proposed attacks can significantly reduce the rewards
received by the attacked agents. | Machine Learning |
What field is the article from? | Title: Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
Abstract: A Bayesian pseudocoreset is a compact synthetic dataset summarizing essential
information of a large-scale dataset and thus can be used as a proxy dataset
for scalable Bayesian inference. Typically, a Bayesian pseudocoreset is
constructed by minimizing a divergence measure between the posterior
conditioning on the pseudocoreset and the posterior conditioning on the full
dataset. However, evaluating the divergence can be challenging, particularly
for the models like deep neural networks having high-dimensional parameters. In
this paper, we propose a novel Bayesian pseudocoreset construction method that
operates on a function space. Unlike previous methods, which construct and
match the coreset and full data posteriors in the space of model parameters
(weights), our method constructs variational approximations to the coreset
posterior on a function space and matches it to the full data posterior in the
function space. By working directly on the function space, our method could
bypass several challenges that may arise when working on a weight space,
including limited scalability and multi-modality issue. Through various
experiments, we demonstrate that the Bayesian pseudocoresets constructed from
our method enjoys enhanced uncertainty quantification and better robustness
across various model architectures. | Machine Learning |
What field is the article from? | Title: AI for Open Science: A Multi-Agent Perspective for Ethically Translating Data to Knowledge
Abstract: AI for Science (AI4Science), particularly in the form of self-driving labs,
has the potential to sideline human involvement and hinder scientific discovery
within the broader community. While prior research has focused on ensuring the
responsible deployment of AI applications, enhancing security, and ensuring
interpretability, we also propose that promoting openness in AI4Science
discoveries should be carefully considered. In this paper, we introduce the
concept of AI for Open Science (AI4OS) as a multi-agent extension of AI4Science
with the core principle of maximizing open knowledge translation throughout the
scientific enterprise rather than a single organizational unit. We use the
established principles of Knowledge Discovery and Data Mining (KDD) to
formalize a language around AI4OS. We then discuss three principle stages of
knowledge translation embedded in AI4Science systems and detail specific points
where openness can be applied to yield an AI4OS alternative. Lastly, we
formulate a theoretical metric to assess AI4OS with a supporting ethical
argument highlighting its importance. Our goal is that by drawing attention to
AI4OS we can ensure the natural consequence of AI4Science (e.g., self-driving
labs) is a benefit not only for its developers but for society as a whole. | Artificial Intelligence |
What field is the article from? | Title: Machine learning-based malware detection for IoT devices using control-flow data
Abstract: Embedded devices are specialised devices designed for one or only a few
purposes. They are often part of a larger system, through wired or wireless
connection. Those embedded devices that are connected to other computers or
embedded systems through the Internet are called Internet of Things (IoT for
short) devices.
With their widespread usage and their insufficient protection, these devices
are increasingly becoming the target of malware attacks. Companies often cut
corners to save manufacturing costs or misconfigure when producing these
devices. This can be lack of software updates, ports left open or security
defects by design. Although these devices may not be as powerful as a regular
computer, their large number makes them suitable candidates for botnets. Other
types of IoT devices can even cause health problems since there are even
pacemakers connected to the Internet. This means, that without sufficient
defence, even directed assaults are possible against people.
The goal of this thesis project is to provide better security for these
devices with the help of machine learning algorithms and reverse engineering
tools. Specifically, I study the applicability of control-flow related data of
executables for malware detection. I present a malware detection method with
two phases. The first phase extracts control-flow related data using static
binary analysis. The second phase classifies binary executables as either
malicious or benign using a neural network model. I train the model using a
dataset of malicious and benign ARM applications. | Artificial Intelligence |
What field is the article from? | Title: Simple Weak Coresets for Non-Decomposable Classification Measures
Abstract: While coresets have been growing in terms of their application, barring few
exceptions, they have mostly been limited to unsupervised settings. We consider
supervised classification problems, and non-decomposable evaluation measures in
such settings. We show that stratified uniform sampling based coresets have
excellent empirical performance that are backed by theoretical guarantees too.
We focus on the F1 score and Matthews Correlation Coefficient, two widely used
non-decomposable objective functions that are nontrivial to optimize for and
show that uniform coresets attain a lower bound for coreset size, and have good
empirical performance, comparable with ``smarter'' coreset construction
strategies. | Machine Learning |
What field is the article from? | Title: Forecasting Lithium-Ion Battery Longevity with Limited Data Availability: Benchmarking Different Machine Learning Algorithms
Abstract: As the use of Lithium-ion batteries continues to grow, it becomes
increasingly important to be able to predict their remaining useful life. This
work aims to compare the relative performance of different machine learning
algorithms, both traditional machine learning and deep learning, in order to
determine the best-performing algorithms for battery cycle life prediction
based on minimal data. We investigated 14 different machine learning models
that were fed handcrafted features based on statistical data and split into 3
feature groups for testing. For deep learning models, we tested a variety of
neural network models including different configurations of standard Recurrent
Neural Networks, Gated Recurrent Units, and Long Short Term Memory with and
without attention mechanism. Deep learning models were fed multivariate time
series signals based on the raw data for each battery across the first 100
cycles. Our experiments revealed that the machine learning algorithms on
handcrafted features performed particularly well, resulting in 10-20% average
mean absolute percentage error. The best-performing algorithm was the Random
Forest Regressor, which gave a minimum 9.8% mean absolute percentage error.
Traditional machine learning models excelled due to their capability to
comprehend general data set trends. In comparison, deep learning models were
observed to perform particularly poorly on raw, limited data. Algorithms like
GRU and RNNs that focused on capturing medium-range data dependencies were less
adept at recognizing the gradual, slow trends critical for this task. Our
investigation reveals that implementing machine learning models with
hand-crafted features proves to be more effective than advanced deep learning
models for predicting the remaining useful Lithium-ion battery life with
limited data availability. | Machine Learning |
What field is the article from? | Title: Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing
Abstract: With the remarkable advent of text-to-image diffusion models, image editing
methods have become more diverse and continue to evolve. A promising recent
approach in this realm is Delta Denoising Score (DDS) - an image editing
technique based on Score Distillation Sampling (SDS) framework that leverages
the rich generative prior of text-to-image diffusion models. However, relying
solely on the difference between scoring functions is insufficient for
preserving specific structural elements from the original image, a crucial
aspect of image editing. Inspired by the similarity and importance differences
between DDS and the contrastive learning for unpaired image-to-image
translation (CUT), here we present an embarrassingly simple yet very powerful
modification of DDS, called Contrastive Denoising Score (CDS), for latent
diffusion models (LDM). Specifically, to enforce structural correspondence
between the input and output while maintaining the controllability of contents,
we introduce a straightforward approach to regulate structural consistency
using CUT loss within the DDS framework. To calculate this loss, instead of
employing auxiliary networks, we utilize the intermediate features of LDM, in
particular, those from the self-attention layers, which possesses rich spatial
information. Our approach enables zero-shot image-to-image translation and
neural radiance field (NeRF) editing, achieving a well-balanced interplay
between maintaining the structural details and transforming content.
Qualitative results and comparisons demonstrates the effectiveness of our
proposed method. Project page with code is available at
https://hyelinnam.github.io/CDS/. | Computer Vision |
What field is the article from? | Title: On Functional Activations in Deep Neural Networks
Abstract: Background: Deep neural networks have proven to be powerful computational
tools for modeling, prediction, and generation. However, the workings of these
models have generally been opaque. Recent work has shown that the performance
of some models are modulated by overlapping functional networks of connections
within the models. Here the techniques of functional neuroimaging are applied
to an exemplary large language model to probe its functional structure.
Methods: A series of block-designed task-based prompt sequences were generated
to probe the Facebook Galactica-125M model. Tasks included prompts relating to
political science, medical imaging, paleontology, archeology, pathology, and
random strings presented in an off/on/off pattern with prompts about other
random topics. For the generation of each output token, all layer output values
were saved to create an effective time series. General linear models were fit
to the data to identify layer output values which were active with the tasks.
Results: Distinct, overlapping networks were identified with each task. Most
overlap was observed between medical imaging and pathology networks. These
networks were repeatable across repeated performance of related tasks, and
correspondence of identified functional networks and activation in tasks not
used to define the functional networks was shown to accurately identify the
presented task. Conclusion: The techniques of functional neuroimaging can be
applied to deep neural networks as a means to probe their workings. Identified
functional networks hold the potential for use in model alignment, modulation
of model output, and identifying weights to target in fine-tuning. | Artificial Intelligence |
What field is the article from? | Title: Localizing Lying in Llama: Understanding Instructed Dishonesty on True-False Questions Through Prompting, Probing, and Patching
Abstract: Large language models (LLMs) demonstrate significant knowledge through their
outputs, though it is often unclear whether false outputs are due to a lack of
knowledge or dishonesty. In this paper, we investigate instructed dishonesty,
wherein we explicitly prompt LLaMA-2-70b-chat to lie. We perform prompt
engineering to find which prompts best induce lying behavior, and then use
mechanistic interpretability approaches to localize where in the network this
behavior occurs. Using linear probing and activation patching, we localize five
layers that appear especially important for lying. We then find just 46
attention heads within these layers that enable us to causally intervene such
that the lying model instead answers honestly. We show that these interventions
work robustly across many prompts and dataset splits. Overall, our work
contributes a greater understanding of dishonesty in LLMs so that we may hope
to prevent it. | Machine Learning |
What field is the article from? | Title: DIRECT: Deep Active Learning under Imbalance and Label Noise
Abstract: Class imbalance is a prevalent issue in real world machine learning
applications, often leading to poor performance in rare and minority classes.
With an abundance of wild unlabeled data, active learning is perhaps the most
effective technique in solving the problem at its root -- collecting a more
balanced and informative set of labeled examples during annotation. In this
work, we propose a novel algorithm that first identifies the class separation
threshold and then annotate the most uncertain examples from the minority
classes, close to the separation threshold. Through a novel reduction to
one-dimensional active learning, our algorithm DIRECT is able to leverage the
classic active learning literature to address issues such as batch labeling and
tolerance towards label noise. Compared to existing algorithms, our algorithm
saves more than 15\% of the annotation budget compared to state-of-art active
learning algorithm and more than 90\% of annotation budget compared to random
sampling. | Machine Learning |
What field is the article from? | Title: CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models
Abstract: Incorporating a customized object into image generation presents an
attractive feature in text-to-image generation. However, existing
optimization-based and encoder-based methods are hindered by drawbacks such as
time-consuming optimization, insufficient identity preservation, and a
prevalent copy-pasting effect. To overcome these limitations, we introduce
CustomNet, a novel object customization approach that explicitly incorporates
3D novel view synthesis capabilities into the object customization process.
This integration facilitates the adjustment of spatial position relationships
and viewpoints, yielding diverse outputs while effectively preserving object
identity. Moreover, we introduce delicate designs to enable location control
and flexible background control through textual descriptions or specific
user-defined images, overcoming the limitations of existing 3D novel view
synthesis methods. We further leverage a dataset construction pipeline that can
better handle real-world objects and complex backgrounds. Equipped with these
designs, our method facilitates zero-shot object customization without
test-time optimization, offering simultaneous control over the viewpoints,
location, and background. As a result, our CustomNet ensures enhanced identity
preservation and generates diverse, harmonious outputs. | Computer Vision |
What field is the article from? | Title: Energy-based Potential Games for Joint Motion Forecasting and Control
Abstract: This work uses game theory as a mathematical framework to address interaction
modeling in multi-agent motion forecasting and control. Despite its
interpretability, applying game theory to real-world robotics, like automated
driving, faces challenges such as unknown game parameters. To tackle these, we
establish a connection between differential games, optimal control, and
energy-based models, demonstrating how existing approaches can be unified under
our proposed Energy-based Potential Game formulation. Building upon this, we
introduce a new end-to-end learning application that combines neural networks
for game-parameter inference with a differentiable game-theoretic optimization
layer, acting as an inductive bias. The analysis provides empirical evidence
that the game-theoretic layer adds interpretability and improves the predictive
performance of various neural network backbones using two simulations and two
real-world driving datasets. | Machine Learning |
What field is the article from? | Title: Two Complementary Perspectives to Continual Learning: Ask Not Only What to Optimize, But Also How
Abstract: Recent years have seen considerable progress in the continual training of
deep neural networks, predominantly thanks to approaches that add replay or
regularization terms to the loss function to approximate the joint loss over
all tasks so far. However, we show that even with a perfect approximation to
the joint loss, these approaches still suffer from temporary but substantial
forgetting when starting to train on a new task. Motivated by this 'stability
gap', we propose that continual learning strategies should focus not only on
the optimization objective, but also on the way this objective is optimized.
While there is some continual learning work that alters the optimization
trajectory (e.g., using gradient projection techniques), this line of research
is positioned as alternative to improving the optimization objective, while we
argue it should be complementary. To evaluate the merits of our proposition, we
plan to combine replay-approximated joint objectives with gradient
projection-based optimization routines to test whether the addition of the
latter provides benefits in terms of (1) alleviating the stability gap, (2)
increasing the learning efficiency and (3) improving the final learning
outcome. | Machine Learning |
What field is the article from? | Title: Human-Guided Complexity-Controlled Abstractions
Abstract: Neural networks often learn task-specific latent representations that fail to
generalize to novel settings or tasks. Conversely, humans learn discrete
representations (i.e., concepts or words) at a variety of abstraction levels
(e.g., "bird" vs. "sparrow") and deploy the appropriate abstraction based on
task. Inspired by this, we train neural models to generate a spectrum of
discrete representations, and control the complexity of the representations
(roughly, how many bits are allocated for encoding inputs) by tuning the
entropy of the distribution over representations. In finetuning experiments,
using only a small number of labeled examples for a new task, we show that (1)
tuning the representation to a task-appropriate complexity level supports the
highest finetuning performance, and (2) in a human-participant study, users
were able to identify the appropriate complexity level for a downstream task
using visualizations of discrete representations. Our results indicate a
promising direction for rapid model finetuning by leveraging human insight. | Machine Learning |
What field is the article from? | Title: Object-Centric Learning with Slot Mixture Module
Abstract: Object-centric architectures usually apply a differentiable module to the
entire feature map to decompose it into sets of entity representations called
slots. Some of these methods structurally resemble clustering algorithms, where
the cluster's center in latent space serves as a slot representation. Slot
Attention is an example of such a method, acting as a learnable analog of the
soft k-means algorithm. Our work employs a learnable clustering method based on
the Gaussian Mixture Model. Unlike other approaches, we represent slots not
only as centers of clusters but also incorporate information about the distance
between clusters and assigned vectors, leading to more expressive slot
representations. Our experiments demonstrate that using this approach instead
of Slot Attention improves performance in object-centric scenarios, achieving
state-of-the-art results in the set property prediction task. | Machine Learning |
What field is the article from? | Title: INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis
Abstract: Synthesizing information from multiple data sources plays a crucial role in
the practice of modern medicine. Current applications of artificial
intelligence in medicine often focus on single-modality data due to a lack of
publicly available, multimodal medical datasets. To address this limitation, we
introduce INSPECT, which contains de-identified longitudinal records from a
large cohort of patients at risk for pulmonary embolism (PE), along with ground
truth labels for multiple outcomes. INSPECT contains data from 19,402 patients,
including CT images, radiology report impression sections, and structured
electronic health record (EHR) data (i.e. demographics, diagnoses, procedures,
vitals, and medications). Using INSPECT, we develop and release a benchmark for
evaluating several baseline modeling approaches on a variety of important PE
related tasks. We evaluate image-only, EHR-only, and multimodal fusion models.
Trained models and the de-identified dataset are made available for
non-commercial use under a data use agreement. To the best of our knowledge,
INSPECT is the largest multimodal dataset integrating 3D medical imaging and
EHR for reproducible methods evaluation and research. | Machine Learning |
What field is the article from? | Title: Automated Camera Calibration via Homography Estimation with GNNs
Abstract: Over the past few decades, a significant rise of camera-based applications
for traffic monitoring has occurred. Governments and local administrations are
increasingly relying on the data collected from these cameras to enhance road
safety and optimize traffic conditions. However, for effective data
utilization, it is imperative to ensure accurate and automated calibration of
the involved cameras. This paper proposes a novel approach to address this
challenge by leveraging the topological structure of intersections. We propose
a framework involving the generation of a set of synthetic intersection
viewpoint images from a bird's-eye-view image, framed as a graph of virtual
cameras to model these images. Using the capabilities of Graph Neural Networks,
we effectively learn the relationships within this graph, thereby facilitating
the estimation of a homography matrix. This estimation leverages the
neighbourhood representation for any real-world camera and is enhanced by
exploiting multiple images instead of a single match. In turn, the homography
matrix allows the retrieval of extrinsic calibration parameters. As a result,
the proposed framework demonstrates superior performance on both synthetic
datasets and real-world cameras, setting a new state-of-the-art benchmark. | Computer Vision |
What field is the article from? | Title: MultiLoRA: Democratizing LoRA for Better Multi-Task Learning
Abstract: LoRA achieves remarkable resource efficiency and comparable performance when
adapting LLMs for specific tasks. Since ChatGPT demonstrated superior
performance on various tasks, there has been a growing desire to adapt one
model for all tasks. However, the explicit low-rank of LoRA limits the
adaptation performance in complex multi-task scenarios. LoRA is dominated by a
small number of top singular vectors while fine-tuning decomposes into a set of
less important unitary transforms. In this paper, we propose MultiLoRA for
better multi-task adaptation by reducing the dominance of top singular vectors
observed in LoRA. MultiLoRA scales LoRA modules horizontally and change
parameter initialization of adaptation matrices to reduce parameter dependency,
thus yields more balanced unitary subspaces. We unprecedentedly construct
specialized training data by mixing datasets of instruction follow, natural
language understanding, world knowledge, to cover semantically and
syntactically different samples. With only 2.5% of additional parameters,
MultiLoRA outperforms single LoRA counterparts and fine-tuning on multiple
benchmarks and model scales. Further investigation into weight update matrices
of MultiLoRA exhibits reduced dependency on top singular vectors and more
democratic unitary transform contributions. | Machine Learning |
What field is the article from? | Title: Understanding Tool Discovery and Tool Innovation Using Active Inference
Abstract: The ability to invent new tools has been identified as an important facet of
our ability as a species to problem solve in dynamic and novel environments.
While the use of tools by artificial agents presents a challenging task and has
been widely identified as a key goal in the field of autonomous robotics, far
less research has tackled the invention of new tools by agents. In this paper,
(1) we articulate the distinction between tool discovery and tool innovation by
providing a minimal description of the two concepts under the formalism of
active inference. We then (2) apply this description to construct a toy model
of tool innovation by introducing the notion of tool affordances into the
hidden states of the agent's probabilistic generative model. This particular
state factorisation facilitates the ability to not just discover tools but
invent them through the offline induction of an appropriate tool property. We
discuss the implications of these preliminary results and outline future
directions of research. | Artificial Intelligence |
What field is the article from? | Title: Control Risk for Potential Misuse of Artificial Intelligence in Science
Abstract: The expanding application of Artificial Intelligence (AI) in scientific
fields presents unprecedented opportunities for discovery and innovation.
However, this growth is not without risks. AI models in science, if misused,
can amplify risks like creation of harmful substances, or circumvention of
established regulations. In this study, we aim to raise awareness of the
dangers of AI misuse in science, and call for responsible AI development and
use in this domain. We first itemize the risks posed by AI in scientific
contexts, then demonstrate the risks by highlighting real-world examples of
misuse in chemical science. These instances underscore the need for effective
risk management strategies. In response, we propose a system called SciGuard to
control misuse risks for AI models in science. We also propose a red-teaming
benchmark SciMT-Safety to assess the safety of different systems. Our proposed
SciGuard shows the least harmful impact in the assessment without compromising
performance in benign tests. Finally, we highlight the need for a
multidisciplinary and collaborative effort to ensure the safe and ethical use
of AI models in science. We hope that our study can spark productive
discussions on using AI ethically in science among researchers, practitioners,
policymakers, and the public, to maximize benefits and minimize the risks of
misuse. | Artificial Intelligence |
What field is the article from? | Title: Symptom-based Machine Learning Models for the Early Detection of COVID-19: A Narrative Review
Abstract: Despite the widespread testing protocols for COVID-19, there are still
significant challenges in early detection of the disease, which is crucial for
preventing its spread and optimizing patient outcomes. Owing to the limited
testing capacity in resource-strapped settings and the limitations of the
available traditional methods of testing, it has been established that a fast
and efficient strategy is important to fully stop the virus. Machine learning
models can analyze large datasets, incorporating patient-reported symptoms,
clinical data, and medical imaging. Symptom-based detection methods have been
developed to predict COVID-19, and they have shown promising results. In this
paper, we provide an overview of the landscape of symptoms-only machine
learning models for predicting COVID-19, including their performance and
limitations. The review will also examine the performance of symptom-based
models when compared to image-based models. Because different studies used
varying datasets, methodologies, and performance metrics. Selecting the model
that performs best relies on the context and objectives of the research.
However, based on the results, we observed that ensemble classifier performed
exceptionally well in predicting the occurrence of COVID-19 based on patient
symptoms with the highest overall accuracy of 97.88%. Gradient Boosting
Algorithm achieved an AUC (Area Under the Curve) of 0.90 and identified key
features contributing to the decision-making process. Image-based models, as
observed in the analyzed studies, have consistently demonstrated higher
accuracy than symptom-based models, often reaching impressive levels ranging
from 96.09% to as high as 99%. | Machine Learning |
What field is the article from? | Title: Using a Large Language Model to generate a Design Structure Matrix
Abstract: The Design Structure Matrix (DSM) is an established method used in dependency
modelling, especially in the design of complex engineering systems. The
generation of DSM is traditionally carried out through manual means and can
involve interviewing experts to elicit critical system elements and the
relationships between them. Such manual approaches can be time-consuming and
costly. This paper presents a workflow that uses a Large Language Model (LLM)
to support the generation of DSM and improve productivity. A prototype of the
workflow was developed in this work and applied on a diesel engine DSM
published previously. It was found that the prototype could reproduce 357 out
of 462 DSM entries published (i.e. 77.3%), suggesting that the work can aid DSM
generation. A no-code version of the prototype is made available online to
support future research. | Artificial Intelligence |
What field is the article from? | Title: Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection
Abstract: The inability to linearly classify XOR has motivated much of deep learning.
We revisit this age-old problem and show that linear classification of XOR is
indeed possible. Instead of separating data between halfspaces, we propose a
slightly different paradigm, equality separation, that adapts the SVM objective
to distinguish data within or outside the margin. Our classifier can then be
integrated into neural network pipelines with a smooth approximation. From its
properties, we intuit that equality separation is suitable for anomaly
detection. To formalize this notion, we introduce closing numbers, a
quantitative measure on the capacity for classifiers to form closed decision
regions for anomaly detection. Springboarding from this theoretical connection
between binary classification and anomaly detection, we test our hypothesis on
supervised anomaly detection experiments, showing that equality separation can
detect both seen and unseen anomalies. | Machine Learning |
What field is the article from? | Title: Can language agents be alternatives to PPO? A Preliminary Empirical Study On OpenAI Gym
Abstract: The formidable capacity for zero- or few-shot decision-making in language
agents encourages us to pose a compelling question: Can language agents be
alternatives to PPO agents in traditional sequential decision-making tasks? To
investigate this, we first take environments collected in OpenAI Gym as our
testbeds and ground them to textual environments that construct the TextGym
simulator. This allows for straightforward and efficient comparisons between
PPO agents and language agents, given the widespread adoption of OpenAI Gym. To
ensure a fair and effective benchmarking, we introduce $5$ levels of scenario
for accurate domain-knowledge controlling and a unified RL-inspired framework
for language agents. Additionally, we propose an innovative
explore-exploit-guided language (EXE) agent to solve tasks within TextGym.
Through numerical experiments and ablation studies, we extract valuable
insights into the decision-making capabilities of language agents and make a
preliminary evaluation of their potential to be alternatives to PPO in
classical sequential decision-making problems. This paper sheds light on the
performance of language agents and paves the way for future research in this
exciting domain. Our code is publicly available
at~\url{https://github.com/mail-ecnu/Text-Gym-Agents}. | Artificial Intelligence |
What field is the article from? | Title: DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops
Abstract: Recommendation models trained on the user feedback collected from deployed
recommendation systems are commonly biased. User feedback is considerably
affected by the exposure mechanism, as users only provide feedback on the items
exposed to them and passively ignore the unexposed items, thus producing
numerous false negative samples. Inevitably, biases caused by such user
feedback are inherited by new models and amplified via feedback loops.
Moreover, the presence of false negative samples makes negative sampling
difficult and introduces spurious information in the user preference modeling
process of the model. Recent work has investigated the negative impact of
feedback loops and unknown exposure mechanisms on recommendation quality and
user experience, essentially treating them as independent factors and ignoring
their cross-effects. To address these issues, we deeply analyze the data
exposure mechanism from the perspective of data iteration and feedback loops
with the Missing Not At Random (\textbf{MNAR}) assumption, theoretically
demonstrating the existence of an available stabilization factor in the
transformation of the exposure mechanism under the feedback loops. We further
propose Dynamic Personalized Ranking (\textbf{DPR}), an unbiased algorithm that
uses dynamic re-weighting to mitigate the cross-effects of exposure mechanisms
and feedback loops without additional information. Furthermore, we design a
plugin named Universal Anti-False Negative (\textbf{UFN}) to mitigate the
negative impact of the false negative problem. We demonstrate theoretically
that our approach mitigates the negative effects of feedback loops and unknown
exposure mechanisms. Experimental results on real-world datasets demonstrate
that models using DPR can better handle bias accumulation and the universality
of UFN in mainstream loss methods. | Information Retrieval |
What field is the article from? | Title: Optimally Teaching a Linear Behavior Cloning Agent
Abstract: We study optimal teaching of Linear Behavior Cloning (LBC) learners. In this
setup, the teacher can select which states to demonstrate to an LBC learner.
The learner maintains a version space of infinite linear hypotheses consistent
with the demonstration. The goal of the teacher is to teach a realizable target
policy to the learner using minimum number of state demonstrations. This number
is known as the Teaching Dimension(TD). We present a teaching algorithm called
``Teach using Iterative Elimination(TIE)" that achieves instance optimal TD.
However, we also show that finding optimal teaching set computationally is
NP-hard. We further provide an approximation algorithm that guarantees an
approximation ratio of $\log(|A|-1)$ on the teaching dimension. Finally, we
provide experimental results to validate the efficiency and effectiveness of
our algorithm. | Machine Learning |
What field is the article from? | Title: Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data
Abstract: Unsupervised anomaly detection methods are at the forefront of industrial
anomaly detection efforts and have made notable progress. Previous work
primarily used 2D information as input, but multi-modal industrial anomaly
detection based on 3D point clouds and RGB images is just beginning to emerge.
The regular approach involves utilizing large pre-trained models for feature
representation and storing them in memory banks. However, the above methods
require a longer inference time and higher memory usage, which cannot meet the
real-time requirements of the industry. To overcome these issues, we propose a
lightweight dual-branch reconstruction network(DBRN) based on RGB-D input,
learning the decision boundary between normal and abnormal examples. The
requirement for alignment between the two modalities is eliminated by using
depth maps instead of point cloud input. Furthermore, we introduce an
importance scoring module in the discriminative network to assist in fusing
features from these two modalities, thereby obtaining a comprehensive
discriminative result. DBRN achieves 92.8% AUROC with high inference efficiency
on the MVTec 3D-AD dataset without large pre-trained models and memory banks. | Computer Vision |
What field is the article from? | Title: MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval
Abstract: With the success of large-scale visual-language pretraining models and the
wide application of image-text retrieval in industry areas, reducing the model
size and streamlining their terminal-device deployment have become urgently
necessary. The mainstream model structures for image-text retrieval are
single-stream and dual-stream, both aiming to close the semantic gap between
visual and textual modalities. Dual-stream models excel at offline indexing and
fast inference, while single-stream models achieve more accurate cross-model
alignment by employing adequate feature fusion. We propose a multi-teacher
cross-modality alignment distillation (MCAD) technique to integrate the
advantages of single-stream and dual-stream models. By incorporating the fused
single-stream features into the image and text features of the dual-stream
model, we formulate new modified teacher features and logits. Then, we conduct
both logit and feature distillation to boost the capability of the student
dual-stream model, achieving high retrieval performance without increasing
inference complexity. Extensive experiments demonstrate the remarkable
performance and high efficiency of MCAD on image-text retrieval tasks.
Furthermore, we implement a mobile CLIP model on Snapdragon clips with only 93M
running memory and 30ms search latency, without apparent performance
degradation of the original large CLIP. | Computer Vision |
What field is the article from? | Title: ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
Abstract: Out-of-distribution (OOD) detection methods often exploit auxiliary outliers
to train model identifying OOD samples, especially discovering challenging
outliers from auxiliary outliers dataset to improve OOD detection. However,
they may still face limitations in effectively distinguishing between the most
challenging OOD samples that are much like in-distribution (ID) data, i.e.,
ID-like samples. To this end, we propose a novel OOD detection framework that
discovers ID-like outliers using CLIP from the vicinity space of the ID
samples, thus helping to identify these most challenging OOD samples. Then a
prompt learning framework is proposed that utilizes the identified ID-like
outliers to further leverage the capabilities of CLIP for OOD detection.
Benefiting from the powerful CLIP, we only need a small number of ID samples to
learn the prompts of the model without exposing other auxiliary outlier
datasets. By focusing on the most challenging ID-like OOD samples and elegantly
exploiting the capabilities of CLIP, our method achieves superior few-shot
learning performance on various real-world image datasets (e.g., in 4-shot OOD
detection on the ImageNet-1k dataset, our method reduces the average FPR95 by
12.16% and improves the average AUROC by 2.76%, compared to state-of-the-art
methods). | Computer Vision |
What field is the article from? | Title: TOD-Flow: Modeling the Structure of Task-Oriented Dialogues
Abstract: Task-Oriented Dialogue (TOD) systems have become crucial components in
interactive artificial intelligence applications. While recent advances have
capitalized on pre-trained language models (PLMs), they exhibit limitations
regarding transparency and controllability. To address these challenges, we
propose a novel approach focusing on inferring the TOD-Flow graph from dialogue
data annotated with dialog acts, uncovering the underlying task structure in
the form of a graph. The inferred TOD-Flow graph can be easily integrated with
any dialogue model to improve its prediction performance, transparency, and
controllability. Our TOD-Flow graph learns what a model can, should, and should
not predict, effectively reducing the search space and providing a rationale
for the model's prediction. We show that the proposed TOD-Flow graph better
resembles human-annotated graphs compared to prior approaches. Furthermore,
when combined with several dialogue policies and end-to-end dialogue models, we
demonstrate that our approach significantly improves dialog act classification
and end-to-end response generation performance in the MultiWOZ and SGD
benchmarks. Code available at: https://github.com/srsohn/TOD-Flow | Computational Linguistics |
What field is the article from? | Title: Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs
Abstract: Intent-based management will play a critical role in achieving customers'
expectations in the next-generation mobile networks. Traditional methods cannot
perform efficient resource management since they tend to handle each
expectation independently. Existing approaches, e.g., based on multi-agent
reinforcement learning (MARL) allocate resources in an efficient fashion when
there are conflicting expectations on the network slice. However, in reality,
systems are often far more complex to be addressed by a standalone MARL
formulation. Often there exists a hierarchical structure of intent fulfilment
where multiple pre-trained, self-interested agents may need to be further
orchestrated by a supervisor or controller agent. Such agents may arrive in the
system adhoc, which then needs to be orchestrated along with other available
agents. Retraining the whole system every time is often infeasible given the
associated time and cost. Given the challenges, such adhoc coordination of
pre-trained systems could be achieved through an intelligent supervisor agent
which incentivizes pre-trained RL/MARL agents through sets of dynamic contracts
(goals or bonuses) and encourages them to act as a cohesive unit towards
fulfilling a global expectation. Some approaches use a rule-based supervisor
agent and deploy the hierarchical constituent agents sequentially, based on
human-coded rules.
In the current work, we propose a framework whereby pre-trained agents can be
orchestrated in parallel leveraging an AI-based supervisor agent. For this, we
propose to use Adhoc-Teaming approaches which assign optimal goals to the MARL
agents and incentivize them to exhibit certain desired behaviours. Results on
the network emulator show that the proposed approach results in faster and
improved fulfilment of expectations when compared to rule-based approaches and
even generalizes to changes in environments. | Artificial Intelligence |
What field is the article from? | Title: Architecture of Smart Certificates for Web3 Applications Against Cyberthreats in Financial Industry
Abstract: This study addresses the security challenges associated with the current
internet transformations, specifically focusing on emerging technologies such
as blockchain and decentralized storage. It also investigates the role of Web3
applications in shaping the future of the internet. The primary objective is to
propose a novel design for 'smart certificates,' which are digital certificates
that can be programmatically enforced. Utilizing such certificates, an
enterprise can better protect itself from cyberattacks and ensure the security
of its data and systems. Web3 recent security solutions by companies and
projects like Certik, Forta, Slither, and Securify are the equivalent of code
scanning tool that were originally developed for Web1 and Web2 applications,
and definitely not like certificates to help enterprises feel safe against
cyberthreats. We aim to improve the resilience of enterprises' digital
infrastructure by building on top of Web3 application and put methodologies in
place for vulnerability analysis and attack correlation, focusing on
architecture of different layers, Wallet/Client, Application and Smart
Contract, where specific components are provided to identify and predict
threats and risks. Furthermore, Certificate Transparency is used for enhancing
the security, trustworthiness and decentralized management of the certificates,
and detecting misuses, compromises, and malfeasances. | Cryptography and Security |
What field is the article from? | Title: Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation
Abstract: Multi-modal 3D scene understanding has gained considerable attention due to
its wide applications in many areas, such as autonomous driving and
human-computer interaction. Compared to conventional single-modal 3D
understanding, introducing an additional modality not only elevates the
richness and precision of scene interpretation but also ensures a more robust
and resilient understanding. This becomes especially crucial in varied and
challenging environments where solely relying on 3D data might be inadequate.
While there has been a surge in the development of multi-modal 3D methods over
past three years, especially those integrating multi-camera images (3D+2D) and
textual descriptions (3D+language), a comprehensive and in-depth review is
notably absent. In this article, we present a systematic survey of recent
progress to bridge this gap. We begin by briefly introducing a background that
formally defines various 3D multi-modal tasks and summarizes their inherent
challenges. After that, we present a novel taxonomy that delivers a thorough
categorization of existing methods according to modalities and tasks, exploring
their respective strengths and limitations. Furthermore, comparative results of
recent approaches on several benchmark datasets, together with insightful
analysis, are offered. Finally, we discuss the unresolved issues and provide
several potential avenues for future research. | Computer Vision |
What field is the article from? | Title: Towards Improving Robustness Against Common Corruptions using Mixture of Class Specific Experts
Abstract: Neural networks have demonstrated significant accuracy across various
domains, yet their vulnerability to subtle input alterations remains a
persistent challenge. Conventional methods like data augmentation, while
effective to some extent, fall short in addressing unforeseen corruptions,
limiting the adaptability of neural networks in real-world scenarios. In
response, this paper introduces a novel paradigm known as the Mixture of
Class-Specific Expert Architecture. The approach involves disentangling feature
learning for individual classes, offering a nuanced enhancement in scalability
and overall performance. By training dedicated network segments for each class
and subsequently aggregating their outputs, the proposed architecture aims to
mitigate vulnerabilities associated with common neural network structures. The
study underscores the importance of comprehensive evaluation methodologies,
advocating for the incorporation of benchmarks like the common corruptions
benchmark. This inclusion provides nuanced insights into the vulnerabilities of
neural networks, especially concerning their generalization capabilities and
robustness to unforeseen distortions. The research aligns with the broader
objective of advancing the development of highly robust learning systems
capable of nuanced reasoning across diverse and challenging real-world
scenarios. Through this contribution, the paper aims to foster a deeper
understanding of neural network limitations and proposes a practical approach
to enhance their resilience in the face of evolving and unpredictable
conditions. | Machine Learning |
What field is the article from? | Title: FERGI: Automatic Annotation of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reaction
Abstract: Researchers have proposed to use data of human preference feedback to
fine-tune text-to-image generative models. However, the scalability of human
feedback collection has been limited by its reliance on manual annotation.
Therefore, we develop and test a method to automatically annotate user
preferences from their spontaneous facial expression reaction to the generated
images. We collect a dataset of Facial Expression Reaction to Generated Images
(FERGI) and show that the activations of multiple facial action units (AUs) are
highly correlated with user evaluations of the generated images. Specifically,
AU4 (brow lowerer) is most consistently reflective of negative evaluations of
the generated image. This can be useful in two ways. Firstly, we can
automatically annotate user preferences between image pairs with substantial
difference in AU4 responses to them with an accuracy significantly
outperforming state-of-the-art scoring models. Secondly, directly integrating
the AU4 responses with the scoring models improves their consistency with human
preferences. Additionally, the AU4 response best reflects the user's evaluation
of the image fidelity, making it complementary to the state-of-the-art scoring
models, which are generally better at reflecting image-text alignment. Finally,
this method of automatic annotation with facial expression analysis can be
potentially generalized to other generation tasks. The code is available at
https://github.com/ShuangquanFeng/FERGI, and the dataset is also available at
the same link for research purposes. | Computer Vision |
What field is the article from? | Title: Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike
Abstract: We use deep reinforcement learning (RL) to optimize a weapons to target
assignment (WTA) policy for multi-vehicle hypersonic strike against multiple
targets. The objective is to maximize the total value of destroyed targets in
each episode. Each randomly generated episode varies the number and initial
conditions of the hypersonic strike weapons (HSW) and targets, the value
distribution of the targets, and the probability of a HSW being intercepted. We
compare the performance of this WTA policy to that of a benchmark WTA policy
derived using non-linear integer programming (NLIP), and find that the RL WTA
policy gives near optimal performance with a 1000X speedup in computation time,
allowing real time operation that facilitates autonomous decision making in the
mission end game. | Artificial Intelligence |
What field is the article from? | Title: Fusion of Deep and Shallow Features for Face Kinship Verification
Abstract: Kinship verification from face images is a novel and formidable challenge in
the realms of pattern recognition and computer vision. This work makes notable
contributions by incorporating a preprocessing technique known as Multiscale
Retinex (MSR), which enhances image quality. Our approach harnesses the
strength of complementary deep (VGG16) and shallow texture descriptors (BSIF)
by combining them at the score level using Logistic Regression (LR) technique.
We assess the effectiveness of our approach by conducting comprehensive
experiments on three challenging kinship datasets: Cornell Kin Face, UB Kin
Face and TS Kin Face | Computer Vision |
What field is the article from? | Title: AI Chatbot for Generating Episodic Future Thinking (EFT) Cue Texts for Health
Abstract: We describe an AI-powered chatbot to aid with health improvement by
generating Episodic Future Thinking (EFT) cue texts that should reduce delay
discounting. In prior studies, EFT has been shown to address maladaptive health
behaviors. Those studies involved participants, working with researchers,
vividly imagining future events, and writing a description that they
subsequently will frequently review, to ensure a shift from an inclination
towards immediate rewards. That should promote behavior change, aiding in
health tasks such as treatment adherence and lifestyle modifications. The AI
chatbot is designed to guide users in generating personalized EFTs, automating
the current labor-intensive interview-based process. This can enhance the
efficiency of EFT interventions and make them more accessible, targeting
specifically those with limited educational backgrounds or communication
challenges. By leveraging AI for EFT intervention, we anticipate broadened
access and improved health outcomes across diverse populations | Human-Computer Interaction |
What field is the article from? | Title: AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models
Abstract: Large language models(LLMs) exhibit excellent performance across a variety of
tasks, but they come with significant computational and storage costs.
Quantizing these models is an effective way to alleviate this issue. However,
existing methods struggle to strike a balance between model accuracy and
hardware efficiency. This is where we introduce AWEQ, a post-training method
that requires no additional training overhead. AWEQ excels in both
ultra-low-bit quantization and 8-bit weight and activation (W8A8) quantization.
There is an observation that weight quantization is less challenging than
activation quantization. AWEQ transfers the difficulty of activation
quantization to weights using channel equalization, achieving a balance between
the quantization difficulties of both, and thereby maximizing performance. We
have further refined the equalization method to mitigate quantization bias
error, ensuring the robustness of the model. Extensive experiments on popular
models such as LLaMA and OPT demonstrate that AWEQ outperforms all existing
post-training quantization methods for large models. | Machine Learning |
What field is the article from? | Title: ChatGPT Application In Summarizing An Evolution Of Deep Learning Techniques In Imaging: A Qualitative Study
Abstract: The pursuit of article or text summarization has captured the attention of
natural language processing (NLP) practitioners, presenting itself as a
formidable challenge. ChatGPT 3.5 exhibits the capacity to condense the content
of up to 3000 tokens into a single page, aiming to retain pivotal information
from a given text across diverse themes. In a conducted qualitative research
endeavor, we selected seven scientific articles and employed the publicly
available ChatGPT service to generate summaries of these articles.
Subsequently, we engaged six co-authors of the articles in a survey, presenting
five questions to evaluate the quality of the summaries compared to the
original content. The findings revealed that the summaries produced by ChatGPT
effectively encapsulated the crucial information present in the articles,
preserving the principal message of each manuscript. Nonetheless, there was a
slight diminishment in the technical depth of the summaries as opposed to the
original articles. As a result, our conclusion underscores ChatGPT's text
summarization capability as a potent tool for extracting essential insights in
a manner more aligned with reporting than purely scientific discourse. | Computational Linguistics |
What field is the article from? | Title: Advances in 3D Neural Stylization: A Survey
Abstract: Modern artificial intelligence provides a novel way of producing digital art
in styles. The expressive power of neural networks enables the realm of visual
style transfer methods, which can be used to edit images, videos, and 3D data
to make them more artistic and diverse. This paper reports on recent advances
in neural stylization for 3D data. We provide a taxonomy for neural stylization
by considering several important design choices, including scene
representation, guidance data, optimization strategies, and output styles.
Building on such taxonomy, our survey first revisits the background of neural
stylization on 2D images, and then provides in-depth discussions on recent
neural stylization methods for 3D data, where we also provide a mini-benchmark
on artistic stylization methods. Based on the insights gained from the survey,
we then discuss open challenges, future research, and potential applications
and impacts of neural stylization. | Computer Vision |
What field is the article from? | Title: Transformers as Graph-to-Graph Models
Abstract: We argue that Transformers are essentially graph-to-graph models, with
sequences just being a special case. Attention weights are functionally
equivalent to graph edges. Our Graph-to-Graph Transformer architecture makes
this ability explicit, by inputting graph edges into the attention weight
computations and predicting graph edges with attention-like functions, thereby
integrating explicit graphs into the latent graphs learned by pretrained
Transformers. Adding iterative graph refinement provides a joint embedding of
input, output, and latent graphs, allowing non-autoregressive graph prediction
to optimise the complete graph without any bespoke pipeline or decoding
strategy. Empirical results show that this architecture achieves
state-of-the-art accuracies for modelling a variety of linguistic structures,
integrating very effectively with the latent linguistic representations learned
by pretraining. | Computational Linguistics |
What field is the article from? | Title: Explainable Fraud Detection with Deep Symbolic Classification
Abstract: There is a growing demand for explainable, transparent, and data-driven
models within the domain of fraud detection. Decisions made by fraud detection
models need to be explainable in the event of a customer dispute. Additionally,
the decision-making process in the model must be transparent to win the trust
of regulators and business stakeholders. At the same time, fraud detection
solutions can benefit from data due to the noisy, dynamic nature of fraud and
the availability of large historical data sets. Finally, fraud detection is
notorious for its class imbalance: there are typically several orders of
magnitude more legitimate transactions than fraudulent ones. In this paper, we
present Deep Symbolic Classification (DSC), an extension of the Deep Symbolic
Regression framework to classification problems. DSC casts classification as a
search problem in the space of all analytic functions composed of a vocabulary
of variables, constants, and operations and optimizes for an arbitrary
evaluation metric directly. The search is guided by a deep neural network
trained with reinforcement learning. Because the functions are mathematical
expressions that are in closed-form and concise, the model is inherently
explainable both at the level of a single classification decision and the
model's decision process. Furthermore, the class imbalance problem is
successfully addressed by optimizing for metrics that are robust to class
imbalance such as the F1 score. This eliminates the need for oversampling and
undersampling techniques that plague traditional approaches. Finally, the model
allows to explicitly balance between the prediction accuracy and the
explainability. An evaluation on the PaySim data set demonstrates competitive
predictive performance with state-of-the-art models, while surpassing them in
terms of explainability. This establishes DSC as a promising model for fraud
detection systems. | Machine Learning |
What field is the article from? | Title: Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning
Abstract: Despite the dramatic progress in Large Language Model (LLM) development, LLMs
often provide seemingly plausible but not factual information, often referred
to as hallucinations. Retrieval-augmented LLMs provide a non-parametric
approach to solve these issues by retrieving relevant information from external
data sources and augment the training process. These models help to trace
evidence from an externally provided knowledge base allowing the model
predictions to be better interpreted and verified. In this work, we critically
evaluate these models in their ability to perform in scientific document
reasoning tasks. To this end, we tuned multiple such model variants with
science-focused instructions and evaluated them on a scientific document
reasoning benchmark for the usefulness of the retrieved document passages. Our
findings suggest that models justify predictions in science tasks with
fabricated evidence and leveraging scientific corpus as pretraining data does
not alleviate the risk of evidence fabrication. | Computational Linguistics |
What field is the article from? | Title: Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
Abstract: Time series forecasting has played the key role in different industrial,
including finance, traffic, energy, and healthcare domains. While existing
literatures have designed many sophisticated architectures based on RNNs, GNNs,
or Transformers, another kind of approaches based on multi-layer perceptrons
(MLPs) are proposed with simple structure, low complexity, and {superior
performance}. However, most MLP-based forecasting methods suffer from the
point-wise mappings and information bottleneck, which largely hinders the
forecasting performance. To overcome this problem, we explore a novel direction
of applying MLPs in the frequency domain for time series forecasting. We
investigate the learned patterns of frequency-domain MLPs and discover their
two inherent characteristic benefiting forecasting, (i) global view: frequency
spectrum makes MLPs own a complete view for signals and learn global
dependencies more easily, and (ii) energy compaction: frequency-domain MLPs
concentrate on smaller key part of frequency components with compact signal
energy. Then, we propose FreTS, a simple yet effective architecture built upon
Frequency-domain MLPs for Time Series forecasting. FreTS mainly involves two
stages, (i) Domain Conversion, that transforms time-domain signals into complex
numbers of frequency domain; (ii) Frequency Learning, that performs our
redesigned MLPs for the learning of real and imaginary part of frequency
components. The above stages operated on both inter-series and intra-series
scales further contribute to channel-wise and time-wise dependency learning.
Extensive experiments on 13 real-world benchmarks (including 7 benchmarks for
short-term forecasting and 6 benchmarks for long-term forecasting) demonstrate
our consistent superiority over state-of-the-art methods. | Machine Learning |
What field is the article from? | Title: SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification
Abstract: Chest X-rays are widely used to diagnose thoracic diseases, but the lack of
detailed information about these abnormalities makes it challenging to develop
accurate automated diagnosis systems, which is crucial for early detection and
effective treatment. To address this challenge, we employed deep learning
techniques to identify patterns in chest X-rays that correspond to different
diseases. We conducted experiments on the "ChestX-ray14" dataset using various
pre-trained CNNs, transformers, hybrid(CNN+Transformer) models and classical
models. The best individual model was the CoAtNet, which achieved an area under
the receiver operating characteristic curve (AUROC) of 84.2%. By combining the
predictions of all trained models using a weighted average ensemble where the
weight of each model was determined using differential evolution, we further
improved the AUROC to 85.4%, outperforming other state-of-the-art methods in
this field. Our findings demonstrate the potential of deep learning techniques,
particularly ensemble deep learning, for improving the accuracy of automatic
diagnosis of thoracic diseases from chest X-rays. | Computer Vision |
What field is the article from? | Title: From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces
Abstract: We provide a novel reduction from swap-regret minimization to external-regret
minimization, which improves upon the classical reductions of Blum-Mansour
[BM07] and Stolz-Lugosi [SL05] in that it does not require finiteness of the
space of actions. We show that, whenever there exists a no-external-regret
algorithm for some hypothesis class, there must also exist a no-swap-regret
algorithm for that same class. For the problem of learning with expert advice,
our result implies that it is possible to guarantee that the swap regret is
bounded by {\epsilon} after $\log(N)^{O(1/\epsilon)}$ rounds and with $O(N)$
per iteration complexity, where $N$ is the number of experts, while the
classical reductions of Blum-Mansour and Stolz-Lugosi require $O(N/\epsilon^2)$
rounds and at least $\Omega(N^2)$ per iteration complexity. Our result comes
with an associated lower bound, which -- in contrast to that in [BM07] -- holds
for oblivious and $\ell_1$-constrained adversaries and learners that can employ
distributions over experts, showing that the number of rounds must be
$\tilde\Omega(N/\epsilon^2)$ or exponential in $1/\epsilon$.
Our reduction implies that, if no-regret learning is possible in some game,
then this game must have approximate correlated equilibria, of arbitrarily good
approximation. This strengthens the folklore implication of no-regret learning
that approximate coarse correlated equilibria exist. Importantly, it provides a
sufficient condition for the existence of correlated equilibrium which vastly
extends the requirement that the action set is finite, thus answering a
question left open by [DG22; Ass+23]. Moreover, it answers several outstanding
questions about equilibrium computation and learning in games. | Machine Learning |
What field is the article from? | Title: Improving Biomedical Entity Linking with Retrieval-enhanced Learning
Abstract: Biomedical entity linking (BioEL) has achieved remarkable progress with the
help of pre-trained language models. However, existing BioEL methods usually
struggle to handle rare and difficult entities due to long-tailed distribution.
To address this limitation, we introduce a new scheme $k$NN-BioEL, which
provides a BioEL model with the ability to reference similar instances from the
entire training corpus as clues for prediction, thus improving the
generalization capabilities. Moreover, we design a contrastive learning
objective with dynamic hard negative sampling (DHNS) that improves the quality
of the retrieved neighbors during inference. Extensive experimental results
show that $k$NN-BioEL outperforms state-of-the-art baselines on several
datasets. | Computational Linguistics |
What field is the article from? | Title: On Mask-based Image Set Desensitization with Recognition Support
Abstract: In recent years, Deep Neural Networks (DNN) have emerged as a practical
method for image recognition. The raw data, which contain sensitive
information, are generally exploited within the training process. However, when
the training process is outsourced to a third-party organization, the raw data
should be desensitized before being transferred to protect sensitive
information. Although masks are widely applied to hide important sensitive
information, preventing inpainting masked images is critical, which may restore
the sensitive information. The corresponding models should be adjusted for the
masked images to reduce the degradation of the performance for recognition or
classification tasks due to the desensitization of images. In this paper, we
propose a mask-based image desensitization approach while supporting
recognition. This approach consists of a mask generation algorithm and a model
adjustment method. We propose exploiting an interpretation algorithm to
maintain critical information for the recognition task in the mask generation
algorithm. In addition, we propose a feature selection masknet as the model
adjustment method to improve the performance based on the masked images.
Extensive experimentation results based on multiple image datasets reveal
significant advantages (up to 9.34% in terms of accuracy) of our approach for
image desensitization while supporting recognition. | Computer Vision |
What field is the article from? | Title: Loss Balancing for Fair Supervised Learning
Abstract: Supervised learning models have been used in various domains such as lending,
college admission, face recognition, natural language processing, etc. However,
they may inherit pre-existing biases from training data and exhibit
discrimination against protected social groups. Various fairness notions have
been proposed to address unfairness issues. In this work, we focus on Equalized
Loss (EL), a fairness notion that requires the expected loss to be
(approximately) equalized across different groups. Imposing EL on the learning
process leads to a non-convex optimization problem even if the loss function is
convex, and the existing fair learning algorithms cannot properly be adopted to
find the fair predictor under the EL constraint. This paper introduces an
algorithm that can leverage off-the-shelf convex programming tools (e.g.,
CVXPY) to efficiently find the global optimum of this non-convex optimization.
In particular, we propose the ELminimizer algorithm, which finds the optimal
fair predictor under EL by reducing the non-convex optimization to a sequence
of convex optimization problems. We theoretically prove that our algorithm
finds the global optimal solution under certain conditions. Then, we support
our theoretical results through several empirical studies. | Machine Learning |
What field is the article from? | Title: Radar Perception in Autonomous Driving: Exploring Different Data Representations
Abstract: With the rapid advancements of sensor technology and deep learning,
autonomous driving systems are providing safe and efficient access to
intelligent vehicles as well as intelligent transportation. Among these
equipped sensors, the radar sensor plays a crucial role in providing robust
perception information in diverse environmental conditions. This review focuses
on exploring different radar data representations utilized in autonomous
driving systems. Firstly, we introduce the capabilities and limitations of the
radar sensor by examining the working principles of radar perception and signal
processing of radar measurements. Then, we delve into the generation process of
five radar representations, including the ADC signal, radar tensor, point
cloud, grid map, and micro-Doppler signature. For each radar representation, we
examine the related datasets, methods, advantages and limitations. Furthermore,
we discuss the challenges faced in these data representations and propose
potential research directions. Above all, this comprehensive review offers an
in-depth insight into how these representations enhance autonomous system
capabilities, providing guidance for radar perception researchers. To
facilitate retrieval and comparison of different data representations, datasets
and methods, we provide an interactive website at
https://radar-camera-fusion.github.io/radar. | Computer Vision |
What field is the article from? | Title: Non-autoregressive Streaming Transformer for Simultaneous Translation
Abstract: Simultaneous machine translation (SiMT) models are trained to strike a
balance between latency and translation quality. However, training these models
to achieve high quality while maintaining low latency often leads to a tendency
for aggressive anticipation. We argue that such issue stems from the
autoregressive architecture upon which most existing SiMT models are built. To
address those issues, we propose non-autoregressive streaming Transformer
(NAST) which comprises a unidirectional encoder and a non-autoregressive
decoder with intra-chunk parallelism. We enable NAST to generate the blank
token or repetitive tokens to adjust its READ/WRITE strategy flexibly, and
train it to maximize the non-monotonic latent alignment with an alignment-based
latency loss. Experiments on various SiMT benchmarks demonstrate that NAST
outperforms previous strong autoregressive SiMT baselines. | Computational Linguistics |
What field is the article from? | Title: Recognize Any Regions
Abstract: Understanding the semantics of individual regions or patches within
unconstrained images, such as in open-world object detection, represents a
critical yet challenging task in computer vision. Building on the success of
powerful image-level vision-language (ViL) foundation models like CLIP, recent
efforts have sought to harness their capabilities by either training a
contrastive model from scratch with an extensive collection of region-label
pairs or aligning the outputs of a detection model with image-level
representations of region proposals. Despite notable progress, these approaches
are plagued by computationally intensive training requirements, susceptibility
to data noise, and deficiency in contextual information. To address these
limitations, we explore the synergistic potential of off-the-shelf foundation
models, leveraging their respective strengths in localization and semantics. We
introduce a novel, generic, and efficient region recognition architecture,
named RegionSpot, designed to integrate position-aware localization knowledge
from a localization foundation model (e.g., SAM) with semantic information
extracted from a ViL model (e.g., CLIP). To fully exploit pretrained knowledge
while minimizing training overhead, we keep both foundation models frozen,
focusing optimization efforts solely on a lightweight attention-based knowledge
integration module. Through extensive experiments in the context of open-world
object recognition, our RegionSpot demonstrates significant performance
improvements over prior alternatives, while also providing substantial
computational savings. For instance, training our model with 3 million data in
a single day using 8 V100 GPUs. Our model outperforms GLIP by 6.5 % in mean
average precision (mAP), with an even larger margin by 14.8 % for more
challenging and rare categories. | Computer Vision |
What field is the article from? | Title: Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems
Abstract: Artificial Intelligence (AI) systems such as autonomous vehicles, facial
recognition, and speech recognition systems are increasingly integrated into
our daily lives. However, despite their utility, these AI systems are
vulnerable to a wide range of attacks such as adversarial, backdoor, data
poisoning, membership inference, model inversion, and model stealing attacks.
In particular, numerous attacks are designed to target a particular model or
system, yet their effects can spread to additional targets, referred to as
transferable attacks. Although considerable efforts have been directed toward
developing transferable attacks, a holistic understanding of the advancements
in transferable attacks remains elusive. In this paper, we comprehensively
explore learning-based attacks from the perspective of transferability,
particularly within the context of cyber-physical security. We delve into
different domains -- the image, text, graph, audio, and video domains -- to
highlight the ubiquitous and pervasive nature of transferable attacks. This
paper categorizes and reviews the architecture of existing attacks from various
viewpoints: data, process, model, and system. We further examine the
implications of transferable attacks in practical scenarios such as autonomous
driving, speech recognition, and large language models (LLMs). Additionally, we
outline the potential research directions to encourage efforts in exploring the
landscape of transferable attacks. This survey offers a holistic understanding
of the prevailing transferable attacks and their impacts across different
domains. | Cryptography and Security |
What field is the article from? | Title: Large Language Model is a Good Policy Teacher for Training Reinforcement Learning Agents
Abstract: Recent studies have shown that Large Language Models (LLMs) can be utilized
for solving complex sequential decision-making tasks by providing high-level
instructions. However, LLM-based agents face limitations in real-time dynamic
environments due to their lack of specialization in solving specific target
problems. Moreover, the deployment of such LLM-based agents is both costly and
time-consuming in practical scenarios. In this paper, we introduce a novel
framework that addresses these challenges by training a smaller scale
specialized student agent using instructions from an LLM-based teacher agent.
By leveraging guided actions provided by the teachers, the prior knowledge of
the LLM is distilled into the local student model. Consequently, the student
agent can be trained with significantly less data. Furthermore, subsequent
training with environment feedback empowers the student agents to surpass the
capabilities of their teachers. We conducted experiments on three challenging
MiniGrid environments to evaluate the effectiveness of our framework. The
results demonstrate that our approach enhances sample efficiency and achieves
superior performance compared to baseline methods. Our code is available at
https://github.com/ZJLAB-AMMI/LLM4Teach. | Artificial Intelligence |
What field is the article from? | Title: Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition
Abstract: Large Language Models (LLMs) are deployed in interactive contexts with direct
user engagement, such as chatbots and writing assistants. These deployments are
vulnerable to prompt injection and jailbreaking (collectively, prompt hacking),
in which models are manipulated to ignore their original instructions and
follow potentially malicious ones. Although widely acknowledged as a
significant security threat, there is a dearth of large-scale resources and
quantitative studies on prompt hacking. To address this lacuna, we launch a
global prompt hacking competition, which allows for free-form human input
attacks. We elicit 600K+ adversarial prompts against three state-of-the-art
LLMs. We describe the dataset, which empirically verifies that current LLMs can
indeed be manipulated via prompt hacking. We also present a comprehensive
taxonomical ontology of the types of adversarial prompts. | Cryptography and Security |
What field is the article from? | Title: LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis
Abstract: Background and objectives: Dynamic handwriting analysis, due to its
non-invasive and readily accessible nature, has recently emerged as a vital
adjunctive method for the early diagnosis of Parkinson's disease. In this
study, we design a compact and efficient network architecture to analyse the
distinctive handwriting patterns of patients' dynamic handwriting signals,
thereby providing an objective identification for the Parkinson's disease
diagnosis.
Methods: The proposed network is based on a hybrid deep learning approach
that fully leverages the advantages of both long short-term memory (LSTM) and
convolutional neural networks (CNNs). Specifically, the LSTM block is adopted
to extract the time-varying features, while the CNN-based block is implemented
using one-dimensional convolution for low computational cost. Moreover, the
hybrid model architecture is continuously refined under ablation studies for
superior performance. Finally, we evaluate the proposed method with its
generalization under a five-fold cross-validation, which validates its
efficiency and robustness.
Results: The proposed network demonstrates its versatility by achieving
impressive classification accuracies on both our new DraWritePD dataset
($96.2\%$) and the well-established PaHaW dataset ($90.7\%$). Moreover, the
network architecture also stands out for its excellent lightweight design,
occupying a mere $0.084$M of parameters, with a total of only $0.59$M
floating-point operations. It also exhibits near real-time CPU inference
performance, with inference times ranging from $0.106$ to $0.220$s.
Conclusions: We present a series of experiments with extensive analysis,
which systematically demonstrate the effectiveness and efficiency of the
proposed hybrid neural network in extracting distinctive handwriting patterns
for precise diagnosis of Parkinson's disease. | Artificial Intelligence |
What field is the article from? | Title: CommunityAI: Towards Community-based Federated Learning
Abstract: Federated Learning (FL) has emerged as a promising paradigm to train machine
learning models collaboratively while preserving data privacy. However, its
widespread adoption faces several challenges, including scalability,
heterogeneous data and devices, resource constraints, and security concerns.
Despite its promise, FL has not been specifically adapted for community
domains, primarily due to the wide-ranging differences in data types and
context, devices and operational conditions, environmental factors, and
stakeholders. In response to these challenges, we present a novel framework for
Community-based Federated Learning called CommunityAI. CommunityAI enables
participants to be organized into communities based on their shared interests,
expertise, or data characteristics. Community participants collectively
contribute to training and refining learning models while maintaining data and
participant privacy within their respective groups. Within this paper, we
discuss the conceptual architecture, system requirements, processes, and future
challenges that must be solved. Finally, our goal within this paper is to
present our vision regarding enabling a collaborative learning process within
various communities. | Machine Learning |
What field is the article from? | Title: Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators
Abstract: Extensive efforts in automated approaches for content moderation have been
focused on developing models to identify toxic, offensive, and hateful content
-- with the aim of lightening the load for moderators. Yet, it remains
uncertain whether improvements on those tasks truly address the needs that
moderators have in accomplishing their work. In this paper, we surface the gaps
between past research efforts that have aimed to provide automation for aspects
of the content moderation task, and the needs of volunteer content moderators.
To do so, we conduct a model review on Hugging Face to reveal the availability
of models to cover various moderation rules and guidelines. We further put
state-of-the-art LLMs to the test (GPT-4 and Llama-2), evaluating how well
these models perform in flagging violations of platform rules. Overall, we
observe a non-trivial gap, as missing developed models and LLMs exhibit low
recall on a significant portion of the rules. | Computational Linguistics |
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