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What field is the article from? | Title: The Innovation-to-Occupations Ontology: Linking Business Transformation Initiatives to Occupations and Skills
Abstract: The fast adoption of new technologies forces companies to continuously adapt
their operations making it harder to predict workforce requirements. Several
recent studies have attempted to predict the emergence of new roles and skills
in the labour market from online job ads. This paper aims to present a novel
ontology linking business transformation initiatives to occupations and an
approach to automatically populating it by leveraging embeddings extracted from
job ads and Wikipedia pages on business transformation and emerging
technologies topics. To our knowledge, no previous research explicitly links
business transformation initiatives, like the adoption of new technologies or
the entry into new markets, to the roles needed. Our approach successfully
matches occupations to transformation initiatives under ten different
scenarios, five linked to technology adoption and five related to business.
This framework presents an innovative approach to guide enterprises and
educational institutions on the workforce requirements for specific business
transformation initiatives. | Artificial Intelligence |
What field is the article from? | Title: A trainable manifold for accurate approximation with ReLU Networks
Abstract: We present a novel technique for exercising greater control of the weights of
ReLU activated neural networks to produce more accurate function
approximations. Many theoretical works encode complex operations into ReLU
networks using smaller base components. In these works, a common base component
is a constant width approximation to x^2, which has exponentially decaying
error with respect to depth. We extend this block to represent a greater range
of convex one-dimensional functions. We derive a manifold of weights such that
the output of these new networks utilizes exponentially many piecewise-linear
segments. This manifold guides their training process to overcome drawbacks
associated with random initialization and unassisted gradient descent. We train
these networks to approximate functions which do not necessarily lie on the
manifold, showing a significant reduction of error values over conventional
approaches. | Machine Learning |
What field is the article from? | Title: Development of a Legal Document AI-Chatbot
Abstract: With the exponential growth of digital data and the increasing complexity of
legal documentation, there is a pressing need for efficient and intelligent
tools to streamline the handling of legal documents.With the recent
developments in the AI field, especially in chatbots, it cannot be ignored as a
very compelling solution to this problem.An insight into the process of
creating a Legal Documentation AI Chatbot with as many relevant features as
possible within the given time frame is presented.The development of each
component of the chatbot is presented in detail.Each component's workings and
functionality has been discussed.Starting from the build of the Android app and
the Langchain query processing code till the integration of both through a
Flask backend and REST API methods. | Artificial Intelligence |
What field is the article from? | Title: NPCL: Neural Processes for Uncertainty-Aware Continual Learning
Abstract: Continual learning (CL) aims to train deep neural networks efficiently on
streaming data while limiting the forgetting caused by new tasks. However,
learning transferable knowledge with less interference between tasks is
difficult, and real-world deployment of CL models is limited by their inability
to measure predictive uncertainties. To address these issues, we propose
handling CL tasks with neural processes (NPs), a class of meta-learners that
encode different tasks into probabilistic distributions over functions all
while providing reliable uncertainty estimates. Specifically, we propose an
NP-based CL approach (NPCL) with task-specific modules arranged in a
hierarchical latent variable model. We tailor regularizers on the learned
latent distributions to alleviate forgetting. The uncertainty estimation
capabilities of the NPCL can also be used to handle the task head/module
inference challenge in CL. Our experiments show that the NPCL outperforms
previous CL approaches. We validate the effectiveness of uncertainty estimation
in the NPCL for identifying novel data and evaluating instance-level model
confidence. Code is available at \url{https://github.com/srvCodes/NPCL}. | Machine Learning |
What field is the article from? | Title: Dense Retrieval as Indirect Supervision for Large-space Decision Making
Abstract: Many discriminative natural language understanding (NLU) tasks have large
label spaces. Learning such a process of large-space decision making is
particularly challenging due to the lack of training instances per label and
the difficulty of selection among many fine-grained labels. Inspired by dense
retrieval methods for passage finding in open-domain QA, we propose a
reformulation of large-space discriminative NLU tasks as a learning-to-retrieve
task, leading to a novel solution named Dense Decision Retrieval (DDR ).
Instead of predicting fine-grained decisions as logits, DDR adopts a
dual-encoder architecture that learns to predict by retrieving from a decision
thesaurus. This approach not only leverages rich indirect supervision signals
from easy-to-consume learning resources for dense retrieval, it also leads to
enhanced prediction generalizability with a semantically meaningful
representation of the large decision space. When evaluated on tasks with
decision spaces ranging from hundreds to hundred-thousand scales, DDR
outperforms strong baselines greatly by 27.54% in P@1 on two extreme
multi-label classification tasks, 1.17% in F1 score ultra-fine entity typing,
and 1.26% in accuracy on three few-shot intent classification tasks on average.
Code and resources are available at https://github.com/luka-group/DDR | Computational Linguistics |
What field is the article from? | Title: Input Reconstruction Attack against Vertical Federated Large Language Models
Abstract: Recently, large language models (LLMs) have drawn extensive attention from
academia and the public, due to the advent of the ChatGPT. While LLMs show
their astonishing ability in text generation for various tasks, privacy
concerns limit their usage in real-life businesses. More specifically, either
the user's inputs (the user sends the query to the model-hosting server) or the
model (the user downloads the complete model) itself will be revealed during
the usage. Vertical federated learning (VFL) is a promising solution to this
kind of problem. It protects both the user's input and the knowledge of the
model by splitting the model into a bottom part and a top part, which is
maintained by the user and the model provider, respectively. However, in this
paper, we demonstrate that in LLMs, VFL fails to protect the user input since
it is simple and cheap to reconstruct the input from the intermediate
embeddings. Experiments show that even with a commercial GPU, the input
sentence can be reconstructed in only one second. We also discuss several
possible solutions to enhance the privacy of vertical federated LLMs. | Computational Linguistics |
What field is the article from? | Title: GlitchBench: Can large multimodal models detect video game glitches?
Abstract: Large multimodal models (LMMs) have evolved from large language models (LLMs)
to integrate multiple input modalities, such as visual inputs. This integration
augments the capacity of LLMs for tasks requiring visual comprehension and
reasoning. However, the extent and limitations of their enhanced abilities are
not fully understood, especially when it comes to real-world tasks. To address
this gap, we introduce GlitchBench, a novel benchmark derived from video game
quality assurance tasks, to test and evaluate the reasoning capabilities of
LMMs. Our benchmark is curated from a variety of unusual and glitched scenarios
from video games and aims to challenge both the visual and linguistic reasoning
powers of LMMs in detecting and interpreting out-of-the-ordinary events. We
evaluate multiple state-of-the-art LMMs, and we show that GlitchBench presents
a new challenge for these models. Code and data are available at:
https://glitchbench.github.io/ | Computer Vision |
What field is the article from? | Title: Game Solving with Online Fine-Tuning
Abstract: Game solving is a similar, yet more difficult task than mastering a game.
Solving a game typically means to find the game-theoretic value (outcome given
optimal play), and optionally a full strategy to follow in order to achieve
that outcome. The AlphaZero algorithm has demonstrated super-human level play,
and its powerful policy and value predictions have also served as heuristics in
game solving. However, to solve a game and obtain a full strategy, a winning
response must be found for all possible moves by the losing player. This
includes very poor lines of play from the losing side, for which the AlphaZero
self-play process will not encounter. AlphaZero-based heuristics can be highly
inaccurate when evaluating these out-of-distribution positions, which occur
throughout the entire search. To address this issue, this paper investigates
applying online fine-tuning while searching and proposes two methods to learn
tailor-designed heuristics for game solving. Our experiments show that using
online fine-tuning can solve a series of challenging 7x7 Killall-Go problems,
using only 23.54% of computation time compared to the baseline without online
fine-tuning. Results suggest that the savings scale with problem size. Our
method can further be extended to any tree search algorithm for problem
solving. Our code is available at
https://rlg.iis.sinica.edu.tw/papers/neurips2023-online-fine-tuning-solver. | Artificial Intelligence |
What field is the article from? | Title: Self-Evaluation Improves Selective Generation in Large Language Models
Abstract: Safe deployment of large language models (LLMs) may benefit from a reliable
method for assessing their generated content to determine when to abstain or to
selectively generate. While likelihood-based metrics such as perplexity are
widely employed, recent research has demonstrated the limitations of using
sequence-level probability estimates given by LLMs as reliable indicators of
generation quality. Conversely, LLMs have demonstrated strong calibration at
the token level, particularly when it comes to choosing correct answers in
multiple-choice questions or evaluating true/false statements. In this work, we
reformulate open-ended generation tasks into token-level prediction tasks, and
leverage LLMs' superior calibration at the token level. We instruct an LLM to
self-evaluate its answers, employing either a multi-way comparison or a
point-wise evaluation approach, with the option to include a ``None of the
above'' option to express the model's uncertainty explicitly. We benchmark a
range of scoring methods based on self-evaluation and evaluate their
performance in selective generation using TruthfulQA and TL;DR. Through
experiments with PaLM-2 and GPT-3, we demonstrate that self-evaluation based
scores not only improve accuracy, but also correlate better with the overall
quality of generated content. | Computational Linguistics |
What field is the article from? | Title: Fast ODE-based Sampling for Diffusion Models in Around 5 Steps
Abstract: Sampling from diffusion models can be treated as solving the corresponding
ordinary differential equations (ODEs), with the aim of obtaining an accurate
solution with as few number of function evaluations (NFE) as possible.
Recently, various fast samplers utilizing higher-order ODE solvers have emerged
and achieved better performance than the initial first-order one. However,
these numerical methods inherently result in certain approximation errors,
which significantly degrades sample quality with extremely small NFE (e.g.,
around 5). In contrast, based on the geometric observation that each sampling
trajectory almost lies in a two-dimensional subspace embedded in the ambient
space, we propose Approximate MEan-Direction Solver (AMED-Solver) that
eliminates truncation errors by directly learning the mean direction for fast
diffusion sampling. Besides, our method can be easily used as a plugin to
further improve existing ODE-based samplers. Extensive experiments on image
synthesis with the resolution ranging from 32 to 256 demonstrate the
effectiveness of our method. With only 5 NFE, we achieve 7.14 FID on CIFAR-10,
13.75 FID on ImageNet 64$\times$64, and 12.79 FID on LSUN Bedroom. Our code is
available at https://github.com/zhyzhouu/amed-solver. | Computer Vision |
What field is the article from? | Title: A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence
Abstract: The potential of digital twin technology is yet to be fully realized due to
its diversity and untapped potential. Digital twins enable systems' analysis,
design, optimization, and evolution to be performed digitally or in conjunction
with a cyber-physical approach to improve speed, accuracy, and efficiency over
traditional engineering methods. Industry 4.0, factories of the future, and
digital twins continue to benefit from the technology and provide enhanced
efficiency within existing systems. Due to the lack of information and security
standards associated with the transition to cyber digitization, cybercriminals
have been able to take advantage of the situation. Access to a digital twin of
a product or service is equivalent to threatening the entire collection. There
is a robust interaction between digital twins and artificial intelligence
tools, which leads to strong interaction between these technologies, so it can
be used to improve the cybersecurity of these digital platforms based on their
integration with these technologies. This study aims to investigate the role of
artificial intelligence in providing cybersecurity for digital twin versions of
various industries, as well as the risks associated with these versions. In
addition, this research serves as a road map for researchers and others
interested in cybersecurity and digital security. | Cryptography and Security |
What field is the article from? | Title: Extending Neural Network Verification to a Larger Family of Piece-wise Linear Activation Functions
Abstract: In this paper, we extend an available neural network verification technique
to support a wider class of piece-wise linear activation functions.
Furthermore, we extend the algorithms, which provide in their original form
exact respectively over-approximative results for bounded input sets
represented as start sets, to allow also unbounded input set. We implemented
our algorithms and demonstrated their effectiveness in some case studies. | Machine Learning |
What field is the article from? | Title: FinBTech: Blockchain-Based Video and Voice Authentication System for Enhanced Security in Financial Transactions Utilizing FaceNet512 and Gaussian Mixture Models
Abstract: In the digital age, it is crucial to make sure that financial transactions
are as secure and reliable as possible. This abstract offers a ground-breaking
method that combines smart contracts, blockchain technology, FaceNet512 for
improved face recognition, and Gaussian Mixture Models (GMM) for speech
authentication to create a system for video and audio verification that is
unmatched. Smart contracts and the immutable ledger of the blockchain are
combined to offer a safe and open environment for financial transactions.
FaceNet512 and GMM offer multi-factor biometric authentication simultaneously,
enhancing security to new heights. By combining cutting-edge technology, this
system offers a strong defense against identity theft and illegal access,
establishing a new benchmark for safe financial transactions. | Cryptography and Security |
What field is the article from? | Title: Dynamic Collaborative Filtering for Matrix- and Tensor-based Recommender Systems
Abstract: In production applications of recommender systems, a continuous data flow is
employed to update models in real-time. Many recommender models often require
complete retraining to adapt to new data. In this work, we introduce a novel
collaborative filtering model for sequential problems known as Tucker
Integrator Recommender - TIRecA. TIRecA efficiently updates its parameters
using only the new data segment, allowing incremental addition of new users and
items to the recommender system. To demonstrate the effectiveness of the
proposed model, we conducted experiments on four publicly available datasets:
MovieLens 20M, Amazon Beauty, Amazon Toys and Games, and Steam. Our comparison
with general matrix and tensor-based baselines in terms of prediction quality
and computational time reveals that TIRecA achieves comparable quality to the
baseline methods, while being 10-20 times faster in training time. | Information Retrieval |
What field is the article from? | Title: Personality of AI
Abstract: This research paper delves into the evolving landscape of fine-tuning large
language models (LLMs) to align with human users, extending beyond basic
alignment to propose "personality alignment" for language models in
organizational settings. Acknowledging the impact of training methods on the
formation of undefined personality traits in AI models, the study draws
parallels with human fitting processes using personality tests. Through an
original case study, we demonstrate the necessity of personality fine-tuning
for AIs and raise intriguing questions about applying human-designed tests to
AIs, engineering specialized AI personality tests, and shaping AI personalities
to suit organizational roles. The paper serves as a starting point for
discussions and developments in the burgeoning field of AI personality
alignment, offering a foundational anchor for future exploration in
human-machine teaming and co-existence. | Human-Computer Interaction |
What field is the article from? | Title: Ontology Revision based on Pre-trained Language Models
Abstract: Ontology revision aims to seamlessly incorporate new information into an
existing ontology and plays a crucial role in tasks such as ontology evolution,
ontology maintenance, and ontology alignment. Similar to repair single
ontologies, resolving logical incoherence in the task of ontology revision is
also important and meaningful since incoherence is a main potential factor to
cause inconsistency and reasoning with an inconsistent ontology will obtain
meaningless answers. To deal with this problem, various ontology revision
methods have been proposed to define revision operators and design ranking
strategies for axioms in an ontology. However, they rarely consider axiom
semantics which provides important information to differentiate axioms. On the
other hand, pre-trained models can be utilized to encode axiom semantics, and
have been widely applied in many natural language processing tasks and
ontology-related ones in recent years. Therefore, in this paper, we define four
scoring functions to rank axioms based on a pre-trained model by considering
various information from a rebuttal ontology and its corresponding reliable
ontology. Based on such a scoring function, we propose an ontology revision
algorithm to deal with unsatisfiable concepts at once. If it is hard to resolve
all unsatisfiable concepts in a rebuttal ontology together, an adapted revision
algorithm is designed to deal with them group by group. We conduct experiments
over 19 ontology pairs and compare our algorithms and scoring functions with
existing ones. According to the experiments, it shows that our algorithms could
achieve promising performance. The adapted revision algorithm could improve the
efficiency largely, and at most 96% time could be saved for some ontology
pairs. Some of our scoring functions help a revision algorithm obtain better
results in many cases, especially for the challenging pairs. | Artificial Intelligence |
What field is the article from? | Title: Towards Adaptive RF Fingerprint-based Authentication of IIoT devices
Abstract: As IoT technologies mature, they are increasingly finding their way into more
sensitive domains, such as Medical and Industrial IoT, in which safety and
cyber-security are of great importance. While the number of deployed IoT
devices continues to increase exponentially, they still present severe
cyber-security vulnerabilities. Effective authentication is paramount to
support trustworthy IIoT communications, however, current solutions focus on
upper-layer identity verification or key-based cryptography which are often
inadequate to the heterogeneous IIoT environment. In this work, we present a
first step towards achieving powerful and flexible IIoT device authentication,
by leveraging AI adaptive Radio Frequency Fingerprinting technique selection
and tuning, at the PHY layer for highly accurate device authentication over
challenging RF environments. | Cryptography and Security |
What field is the article from? | Title: Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for now
Abstract: Generative models can produce impressively realistic images. This paper
demonstrates that generated images have geometric features different from those
of real images. We build a set of collections of generated images, prequalified
to fool simple, signal-based classifiers into believing they are real. We then
show that prequalified generated images can be identified reliably by
classifiers that only look at geometric properties. We use three such
classifiers. All three classifiers are denied access to image pixels, and look
only at derived geometric features. The first classifier looks at the
perspective field of the image, the second looks at lines detected in the
image, and the third looks at relations between detected objects and shadows.
Our procedure detects generated images more reliably than SOTA local signal
based detectors, for images from a number of distinct generators. Saliency maps
suggest that the classifiers can identify geometric problems reliably. We
conclude that current generators cannot reliably reproduce geometric properties
of real images. | Computer Vision |
What field is the article from? | Title: Pragmatic Radiology Report Generation
Abstract: When pneumonia is not found on a chest X-ray, should the report describe this
negative observation or omit it? We argue that this question cannot be answered
from the X-ray alone and requires a pragmatic perspective, which captures the
communicative goal that radiology reports serve between radiologists and
patients. However, the standard image-to-text formulation for radiology report
generation fails to incorporate such pragmatic intents. Following this
pragmatic perspective, we demonstrate that the indication, which describes why
a patient comes for an X-ray, drives the mentions of negative observations and
introduce indications as additional input to report generation. With respect to
the output, we develop a framework to identify uninferable information from the
image as a source of model hallucinations, and limit them by cleaning
groundtruth reports. Finally, we use indications and cleaned groundtruth
reports to develop pragmatic models, and show that they outperform existing
methods not only in new pragmatics-inspired metrics (+4.3 Negative F1) but also
in standard metrics (+6.3 Positive F1 and +11.0 BLEU-2). | Computational Linguistics |
What field is the article from? | Title: State-Wise Safe Reinforcement Learning With Pixel Observations
Abstract: In the context of safe exploration, Reinforcement Learning (RL) has long
grappled with the challenges of balancing the tradeoff between maximizing
rewards and minimizing safety violations, particularly in complex environments
with contact-rich or non-smooth dynamics, and when dealing with
high-dimensional pixel observations. Furthermore, incorporating state-wise
safety constraints in the exploration and learning process, where the agent
must avoid unsafe regions without prior knowledge, adds another layer of
complexity. In this paper, we propose a novel pixel-observation safe RL
algorithm that efficiently encodes state-wise safety constraints with unknown
hazard regions through a newly introduced latent barrier-like function learning
mechanism. As a joint learning framework, our approach begins by constructing a
latent dynamics model with low-dimensional latent spaces derived from pixel
observations. We then build and learn a latent barrier-like function on top of
the latent dynamics and conduct policy optimization simultaneously, thereby
improving both safety and the total expected return. Experimental evaluations
on the safety-gym benchmark suite demonstrate that our proposed method
significantly reduces safety violations throughout the training process, and
demonstrates faster safety convergence compared to existing methods while
achieving competitive results in reward return. | Machine Learning |
What field is the article from? | Title: SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation
Abstract: Current Visual-Language Pre-training (VLP) models are vulnerable to
adversarial examples. These adversarial examples present substantial security
risks to VLP models, as they can leverage inherent weaknesses in the models,
resulting in incorrect predictions. In contrast to white-box adversarial
attacks, transfer attacks (where the adversary crafts adversarial examples on a
white-box model to fool another black-box model) are more reflective of
real-world scenarios, thus making them more meaningful for research. By
summarizing and analyzing existing research, we identified two factors that can
influence the efficacy of transfer attacks on VLP models: inter-modal
interaction and data diversity. Based on these insights, we propose a
self-augment-based transfer attack method, termed SA-Attack. Specifically,
during the generation of adversarial images and adversarial texts, we apply
different data augmentation methods to the image modality and text modality,
respectively, with the aim of improving the adversarial transferability of the
generated adversarial images and texts. Experiments conducted on the FLickr30K
and COCO datasets have validated the effectiveness of our method. Our code will
be available after this paper is accepted. | Computer Vision |
What field is the article from? | Title: Weaving Pathways for Justice with GPT: LLM-driven automated drafting of interactive legal applications
Abstract: Can generative AI help us speed up the authoring of tools to help
self-represented litigants?
In this paper, we describe 3 approaches to automating the completion of court
forms: a generative AI approach that uses GPT-3 to iteratively prompt the user
to answer questions, a constrained template-driven approach that uses
GPT-4-turbo to generate a draft of questions that are subject to human review,
and a hybrid method. We use the open source Docassemble platform in all 3
experiments, together with a tool created at Suffolk University Law School
called the Assembly Line Weaver. We conclude that the hybrid model of
constrained automated drafting with human review is best suited to the task of
authoring guided interviews. | Artificial Intelligence |
What field is the article from? | Title: Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games
Abstract: Large Language Models (LLMs) have demonstrated superior performance in
language understanding benchmarks. CALM, a popular approach, leverages
linguistic priors of LLMs -- GPT-2 -- for action candidate recommendations to
improve the performance in text games in Jericho without environment-provided
actions. However, CALM adapts GPT-2 with annotated human gameplays and keeps
the LLM fixed during the learning of the text based games. In this work, we
explore and evaluate updating LLM used for candidate recommendation during the
learning of the text based game as well to mitigate the reliance on the human
annotated gameplays, which are costly to acquire. We observe that by updating
the LLM during learning using carefully selected in-game transitions, we can
reduce the dependency on using human annotated game plays for fine-tuning the
LLMs. We conducted further analysis to study the transferability of the updated
LLMs and observed that transferring in-game trained models to other games did
not result in a consistent transfer. | Computational Linguistics |
What field is the article from? | Title: Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
Abstract: Knowledge tracing (KT) plays a crucial role in computer-aided education and
intelligent tutoring systems, aiming to assess students' knowledge proficiency
by predicting their future performance on new questions based on their past
response records. While existing deep learning knowledge tracing (DLKT) methods
have significantly improved prediction accuracy and achieved state-of-the-art
results, they often suffer from a lack of interpretability. To address this
limitation, current approaches have explored incorporating psychological
influences to achieve more explainable predictions, but they tend to overlook
the potential influences of historical responses. In fact, understanding how
models make predictions based on response influences can enhance the
transparency and trustworthiness of the knowledge tracing process, presenting
an opportunity for a new paradigm of interpretable KT. However, measuring
unobservable response influences is challenging. In this paper, we resort to
counterfactual reasoning that intervenes in each response to answer
\textit{what if a student had answered a question incorrectly that he/she
actually answered correctly, and vice versa}. Based on this, we propose RCKT, a
novel response influence-based counterfactual knowledge tracing framework. RCKT
generates response influences by comparing prediction outcomes from factual
sequences and constructed counterfactual sequences after interventions.
Additionally, we introduce maximization and inference techniques to leverage
accumulated influences from different past responses, further improving the
model's performance and credibility. Extensive experimental results demonstrate
that our RCKT method outperforms state-of-the-art knowledge tracing methods on
four datasets against six baselines, and provides credible interpretations of
response influences. | Computers and Society |
What field is the article from? | Title: Generalizable Imitation Learning Through Pre-Trained Representations
Abstract: In this paper we leverage self-supervised vision transformer models and their
emergent semantic abilities to improve the generalization abilities of
imitation learning policies. We introduce BC-ViT, an imitation learning
algorithm that leverages rich DINO pre-trained Visual Transformer (ViT)
patch-level embeddings to obtain better generalization when learning through
demonstrations. Our learner sees the world by clustering appearance features
into semantic concepts, forming stable keypoints that generalize across a wide
range of appearance variations and object types. We show that this
representation enables generalized behaviour by evaluating imitation learning
across a diverse dataset of object manipulation tasks. Our method, data and
evaluation approach are made available to facilitate further study of
generalization in Imitation Learners. | Robotics |
What field is the article from? | Title: MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Abstract: Code LLMs have emerged as a specialized research field, with remarkable
studies dedicated to enhancing model's coding capabilities through fine-tuning
on pre-trained models. Previous fine-tuning approaches were typically tailored
to specific downstream tasks or scenarios, which meant separate fine-tuning for
each task, requiring extensive training resources and posing challenges in
terms of deployment and maintenance. Furthermore, these approaches failed to
leverage the inherent interconnectedness among different code-related tasks. To
overcome these limitations, we present a multi-task fine-tuning framework,
MFTcoder, that enables simultaneous and parallel fine-tuning on multiple tasks.
By incorporating various loss functions, we effectively address common
challenges in multi-task learning, such as data imbalance, varying difficulty
levels, and inconsistent convergence speeds. Extensive experiments have
conclusively demonstrated that our multi-task fine-tuning approach outperforms
both individual fine-tuning on single tasks and fine-tuning on a mixed ensemble
of tasks. Moreover, MFTcoder offers efficient training capabilities, including
efficient data tokenization modes and PEFT fine-tuning, resulting in
significantly improved speed compared to traditional fine-tuning methods.
MFTcoder seamlessly integrates with several mainstream open-source LLMs, such
as CodeLLama and Qwen. Leveraging the CodeLLama foundation, our MFTcoder
fine-tuned model, \textsc{CodeFuse-CodeLLama-34B}, achieves an impressive
pass@1 score of 74.4\% on the HumaneEval benchmark, surpassing GPT-4
performance (67\%, zero-shot). MFTCoder is open-sourced at
\url{https://github.com/codefuse-ai/MFTCOder} | Machine Learning |
What field is the article from? | Title: XFEVER: Exploring Fact Verification across Languages
Abstract: This paper introduces the Cross-lingual Fact Extraction and VERification
(XFEVER) dataset designed for benchmarking the fact verification models across
different languages. We constructed it by translating the claim and evidence
texts of the Fact Extraction and VERification (FEVER) dataset into six
languages. The training and development sets were translated using machine
translation, whereas the test set includes texts translated by professional
translators and machine-translated texts. Using the XFEVER dataset, two
cross-lingual fact verification scenarios, zero-shot learning and
translate-train learning, are defined, and baseline models for each scenario
are also proposed in this paper. Experimental results show that the
multilingual language model can be used to build fact verification models in
different languages efficiently. However, the performance varies by language
and is somewhat inferior to the English case. We also found that we can
effectively mitigate model miscalibration by considering the prediction
similarity between the English and target languages. The XFEVER dataset, code,
and model checkpoints are available at
https://github.com/nii-yamagishilab/xfever. | Computational Linguistics |
What field is the article from? | Title: From Knowledge Representation to Knowledge Organization and Back
Abstract: Knowledge Representation (KR) and facet-analytical Knowledge Organization
(KO) have been the two most prominent methodologies of data and knowledge
modelling in the Artificial Intelligence community and the Information Science
community, respectively. KR boasts of a robust and scalable ecosystem of
technologies to support knowledge modelling while, often, underemphasizing the
quality of its models (and model-based data). KO, on the other hand, is less
technology-driven but has developed a robust framework of guiding principles
(canons) for ensuring modelling (and model-based data) quality. This paper
elucidates both the KR and facet-analytical KO methodologies in detail and
provides a functional mapping between them. Out of the mapping, the paper
proposes an integrated KO-enriched KR methodology with all the standard
components of a KR methodology plus the guiding canons of modelling quality
provided by KO. The practical benefits of the methodological integration has
been exemplified through a prominent case study of KR-based image annotation
exercise. | Artificial Intelligence |
What field is the article from? | Title: Adversarial Attacks to Reward Machine-based Reinforcement Learning
Abstract: In recent years, Reward Machines (RMs) have stood out as a simple yet
effective automata-based formalism for exposing and exploiting task structure
in reinforcement learning settings. Despite their relevance, little to no
attention has been directed to the study of their security implications and
robustness to adversarial scenarios, likely due to their recent appearance in
the literature. With my thesis, I aim to provide the first analysis of the
security of RM-based reinforcement learning techniques, with the hope of
motivating further research in the field, and I propose and evaluate a novel
class of attacks on RM-based techniques: blinding attacks. | Machine Learning |
What field is the article from? | Title: HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment
Abstract: The strength of a supply chain is an important measure of a country's or
region's technical advancement and overall competitiveness. Establishing supply
chain risk assessment models for effective management and mitigation of
potential risks has become increasingly crucial. As the number of businesses
grows, the important relationships become more complicated and difficult to
measure. This emphasizes the need of extracting relevant information from graph
data. Previously, academics mostly employed knowledge inference to increase the
visibility of links between nodes in the supply chain. However, they have not
solved the data hunger problem of single node feature characteristics. We
propose a hierarchical knowledge transferable graph neural network-based
(HKTGNN) supply chain risk assessment model to address these issues. Our
approach is based on current graph embedding methods for assessing corporate
investment risk assessment. We embed the supply chain network corresponding to
individual goods in the supply chain using the graph embedding module,
resulting in a directed homogeneous graph with just product nodes. This reduces
the complicated supply chain network into a basic product network. It addresses
difficulties using the domain difference knowledge transferable module based on
centrality, which is presented by the premise that supply chain feature
characteristics may be biased in the actual world. Meanwhile, the feature
complement and message passing will alleviate the data hunger problem, which is
driven by domain differences. Our model outperforms in experiments on a
real-world supply chain dataset. We will give an equation to prove that our
comparative experiment is both effective and fair. | Machine Learning |
What field is the article from? | Title: Multi Loss-based Feature Fusion and Top Two Voting Ensemble Decision Strategy for Facial Expression Recognition in the Wild
Abstract: Facial expression recognition (FER) in the wild is a challenging task
affected by the image quality and has attracted broad interest in computer
vision. There is no research using feature fusion and ensemble strategy for FER
simultaneously. Different from previous studies, this paper applies both
internal feature fusion for a single model and feature fusion among multiple
networks, as well as the ensemble strategy. This paper proposes one novel
single model named R18+FAML, as well as one ensemble model named
R18+FAML-FGA-T2V to improve the performance of the FER in the wild. Based on
the structure of ResNet18 (R18), R18+FAML combines internal Feature fusion and
three Attention blocks using Multiple Loss functions (FAML) to improve the
diversity of the feature extraction. To improve the performance of R18+FAML, we
propose a Feature fusion among networks based on the Genetic Algorithm (FGA),
which can fuse the convolution kernels for feature extraction of multiple
networks. On the basis of R18+FAML and FGA, we propose one ensemble strategy,
i.e., the Top Two Voting (T2V) to support the classification of FER, which can
consider more classification information comprehensively. Combining the above
strategies, R18+FAML-FGA-T2V can focus on the main expression-aware areas.
Extensive experiments demonstrate that our single model R18+FAML and the
ensemble model R18+FAML-FGA-T2V achieve the accuracies of $\left( 90.32, 62.17,
65.83 \right)\%$ and $\left( 91.59, 63.27, 66.63 \right)\%$ on three
challenging unbalanced FER datasets RAF-DB, AffectNet-8 and AffectNet-7
respectively, both outperforming the state-of-the-art results. | Computer Vision |
What field is the article from? | Title: From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning
Abstract: Pre-trained language models (PLMs) have shown impressive performance in
various language tasks. However, they are prone to spurious correlations, and
often generate illusory information. In real-world applications, PLMs should
justify decisions with formalized, coherent reasoning chains, but this
challenge remains under-explored. Cognitive psychology theorizes that humans
are capable of utilizing fast and intuitive heuristic thinking to make
decisions based on past experience, then rationalizing the decisions through
slower and deliberative analytic reasoning. We incorporate these interlinked
dual processes in fine-tuning and in-context learning with PLMs, applying them
to two language understanding tasks that require coherent physical commonsense
reasoning. We show that our proposed Heuristic-Analytic Reasoning (HAR)
strategies drastically improve the coherence of rationalizations for model
decisions, yielding state-of-the-art results on Tiered Reasoning for Intuitive
Physics (TRIP). We also find that this improved coherence is a direct result of
more faithful attention to relevant language context in each step of reasoning.
Our findings suggest that human-like reasoning strategies can effectively
improve the coherence and reliability of PLM reasoning. | Computational Linguistics |
What field is the article from? | Title: Exploring Causal Learning through Graph Neural Networks: An In-depth Review
Abstract: In machine learning, exploring data correlations to predict outcomes is a
fundamental task. Recognizing causal relationships embedded within data is
pivotal for a comprehensive understanding of system dynamics, the significance
of which is paramount in data-driven decision-making processes. Beyond
traditional methods, there has been a surge in the use of graph neural networks
(GNNs) for causal learning, given their capabilities as universal data
approximators. Thus, a thorough review of the advancements in causal learning
using GNNs is both relevant and timely. To structure this review, we introduce
a novel taxonomy that encompasses various state-of-the-art GNN methods employed
in studying causality. GNNs are further categorized based on their applications
in the causality domain. We further provide an exhaustive compilation of
datasets integral to causal learning with GNNs to serve as a resource for
practical study. This review also touches upon the application of causal
learning across diverse sectors. We conclude the review with insights into
potential challenges and promising avenues for future exploration in this
rapidly evolving field of machine learning. | Machine Learning |
What field is the article from? | Title: Bias in Evaluation Processes: An Optimization-Based Model
Abstract: Biases with respect to socially-salient attributes of individuals have been
well documented in evaluation processes used in settings such as admissions and
hiring. We view such an evaluation process as a transformation of a
distribution of the true utility of an individual for a task to an observed
distribution and model it as a solution to a loss minimization problem subject
to an information constraint. Our model has two parameters that have been
identified as factors leading to biases: the resource-information trade-off
parameter in the information constraint and the risk-averseness parameter in
the loss function. We characterize the distributions that arise from our model
and study the effect of the parameters on the observed distribution. The
outputs of our model enrich the class of distributions that can be used to
capture variation across groups in the observed evaluations. We empirically
validate our model by fitting real-world datasets and use it to study the
effect of interventions in a downstream selection task. These results
contribute to an understanding of the emergence of bias in evaluation processes
and provide tools to guide the deployment of interventions to mitigate biases. | Computers and Society |
What field is the article from? | Title: Vision-Language Integration in Multimodal Video Transformers (Partially) Aligns with the Brain
Abstract: Integrating information from multiple modalities is arguably one of the
essential prerequisites for grounding artificial intelligence systems with an
understanding of the real world. Recent advances in video transformers that
jointly learn from vision, text, and sound over time have made some progress
toward this goal, but the degree to which these models integrate information
from modalities still remains unclear. In this work, we present a promising
approach for probing a pre-trained multimodal video transformer model by
leveraging neuroscientific evidence of multimodal information processing in the
brain. Using brain recordings of participants watching a popular TV show, we
analyze the effects of multi-modal connections and interactions in a
pre-trained multi-modal video transformer on the alignment with uni- and
multi-modal brain regions. We find evidence that vision enhances masked
prediction performance during language processing, providing support that
cross-modal representations in models can benefit individual modalities.
However, we don't find evidence of brain-relevant information captured by the
joint multi-modal transformer representations beyond that captured by all of
the individual modalities. We finally show that the brain alignment of the
pre-trained joint representation can be improved by fine-tuning using a task
that requires vision-language inferences. Overall, our results paint an
optimistic picture of the ability of multi-modal transformers to integrate
vision and language in partially brain-relevant ways but also show that
improving the brain alignment of these models may require new approaches. | Computer Vision |
What field is the article from? | Title: Large Knowledge Model: Perspectives and Challenges
Abstract: Humankind's understanding of the world is fundamentally linked to our
perception and cognition, with \emph{human languages} serving as one of the
major carriers of \emph{world knowledge}. In this vein, \emph{Large Language
Models} (LLMs) like ChatGPT epitomize the pre-training of extensive,
sequence-based world knowledge into neural networks, facilitating the
processing and manipulation of this knowledge in a parametric space. This
article explores large models through the lens of ``knowledge''. We initially
investigate the role of symbolic knowledge such as Knowledge Graphs (KGs) in
enhancing LLMs, covering aspects like knowledge-augmented language model,
structure-inducing pre-training, knowledgeable prompts, structured CoT,
knowledge editing, semantic tools for LLM and knowledgeable AI agents.
Subsequently, we examine how LLMs can amplify traditional symbolic knowledge
bases, encompassing aspects like using LLM as KG builder and controller,
structured knowledge pretraining, LLM-enhanced symbolic reasoning, and the
amalgamation of perception with cognition. Considering the intricate nature of
human knowledge, we advocate for the creation of \emph{Large Knowledge Models}
(LKM), specifically engineered to manage diversified spectrum of knowledge
structures. This ambitious undertaking could entail several key challenges,
such as disentangling knowledge representation from language models,
restructuring pre-training with structured knowledge, and building large
commonsense models, among others. We finally propose a five-``A'' principle to
distinguish the concept of LKM. | Artificial Intelligence |
What field is the article from? | Title: Compensation Sampling for Improved Convergence in Diffusion Models
Abstract: Diffusion models achieve remarkable quality in image generation, but at a
cost. Iterative denoising requires many time steps to produce high fidelity
images. We argue that the denoising process is crucially limited by an
accumulation of the reconstruction error due to an initial inaccurate
reconstruction of the target data. This leads to lower quality outputs, and
slower convergence. To address this issue, we propose compensation sampling to
guide the generation towards the target domain. We introduce a compensation
term, implemented as a U-Net, which adds negligible computation overhead during
training and, optionally, inference. Our approach is flexible and we
demonstrate its application in unconditional generation, face inpainting, and
face de-occlusion using benchmark datasets CIFAR-10, CelebA, CelebA-HQ,
FFHQ-256, and FSG. Our approach consistently yields state-of-the-art results in
terms of image quality, while accelerating the denoising process to converge
during training by up to an order of magnitude. | Computer Vision |
What field is the article from? | Title: Fingerprint Matching with Localized Deep Representation
Abstract: Compared to minutia-based fingerprint representations, fixed-length
representations are attractive due to simple and efficient matching. However,
fixed-length fingerprint representations are limited in accuracy when matching
fingerprints with different visible areas, which can occur due to different
finger poses or acquisition methods. To address this issue, we propose a
localized deep representation of fingerprint, named LDRF. By focusing on the
discriminative characteristics within local regions, LDRF provides a more
robust and accurate fixed-length representation for fingerprints with variable
visible areas. LDRF can be adapted to retain information within any valid area,
making it highly flexible. The matching scores produced by LDRF also exhibit
intuitive statistical characteristics, which led us to propose a matching score
normalization technique to mitigate the uncertainty in the cases of very small
overlapping area. With this new technique, we can maintain a high level of
accuracy and reliability in our fingerprint matching, even as the size of the
database grows rapidly. Our experimental results on 21 datasets containing over
140K fingerprints of various finger poses and impression types show that LDRF
outperforms other fixed-length representations and is robust to sensing
technologies and impression types. Besides, the proposed matching score
normalization effectively reduces the false match rate (FMR) in large-scale
identification experiments comprising over 5.11 million fingerprints.
Specifically, this technique results in a reduction of two orders of magnitude
compared to matching without matching score normalization and five orders of
magnitude compared to prior works. | Computer Vision |
What field is the article from? | Title: Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach
Abstract: Lattice reduction is a combinatorial optimization problem aimed at finding
the most orthogonal basis in a given lattice. In this work, we address lattice
reduction via deep learning methods. We design a deep neural model outputting
factorized unimodular matrices and train it in a self-supervised manner by
penalizing non-orthogonal lattice bases. We incorporate the symmetries of
lattice reduction into the model by making it invariant and equivariant with
respect to appropriate continuous and discrete groups. | Machine Learning |
What field is the article from? | Title: An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph
Abstract: Mapping ongoing news headlines to event-related classes in a rich knowledge
base can be an important component in a knowledge-based event analysis and
forecasting solution. In this paper, we present a methodology for creating a
benchmark dataset of news headlines mapped to event classes in Wikidata, and
resources for the evaluation of methods that perform the mapping. We use the
dataset to study two classes of unsupervised methods for this task: 1)
adaptations of classic entity linking methods, and 2) methods that treat the
problem as a zero-shot text classification problem. For the first approach, we
evaluate off-the-shelf entity linking systems. For the second approach, we
explore a) pre-trained natural language inference (NLI) models, and b)
pre-trained large generative language models. We present the results of our
evaluation, lessons learned, and directions for future work. The dataset and
scripts for evaluation are made publicly available. | Computational Linguistics |
What field is the article from? | Title: Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics
Abstract: The past decade has witnessed the rapid development of geospatial artificial
intelligence (GeoAI) primarily due to the ground-breaking achievements in deep
learning and machine learning. A growing number of scholars from cartography
have demonstrated successfully that GeoAI can accelerate previously complex
cartographic design tasks and even enable cartographic creativity in new ways.
Despite the promise of GeoAI, researchers and practitioners have growing
concerns about the ethical issues of GeoAI for cartography. In this paper, we
conducted a systematic content analysis and narrative synthesis of research
studies integrating GeoAI and cartography to summarize current research and
development trends regarding the usage of GeoAI for cartographic design. Based
on this review and synthesis, we first identify dimensions of GeoAI methods for
cartography such as data sources, data formats, map evaluations, and six
contemporary GeoAI models, each of which serves a variety of cartographic
tasks. These models include decision trees, knowledge graph and semantic web
technologies, deep convolutional neural networks, generative adversarial
networks, graph neural networks, and reinforcement learning. Further, we
summarize seven cartographic design applications where GeoAI have been
effectively employed: generalization, symbolization, typography, map reading,
map interpretation, map analysis, and map production. We also raise five
potential ethical challenges that need to be addressed in the integration of
GeoAI for cartography: commodification, responsibility, privacy, bias, and
(together) transparency, explainability, and provenance. We conclude by
identifying four potential research directions for future cartographic research
with GeoAI: GeoAI-enabled active cartographic symbolism, human-in-the-loop
GeoAI for cartography, GeoAI-based mapping-as-a-service, and generative GeoAI
for cartography. | Human-Computer Interaction |
What field is the article from? | Title: Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger
Abstract: Currently, sample-specific backdoor attacks (SSBAs) are the most advanced and
malicious methods since they can easily circumvent most of the current backdoor
defenses. In this paper, we reveal that SSBAs are not sufficiently stealthy due
to their poisoned-label nature, where users can discover anomalies if they
check the image-label relationship. In particular, we demonstrate that it is
ineffective to directly generalize existing SSBAs to their clean-label variants
by poisoning samples solely from the target class. We reveal that it is
primarily due to two reasons, including \textbf{(1)} the `antagonistic effects'
of ground-truth features and \textbf{(2)} the learning difficulty of
sample-specific features. Accordingly, trigger-related features of existing
SSBAs cannot be effectively learned under the clean-label setting due to their
mild trigger intensity required for ensuring stealthiness. We argue that the
intensity constraint of existing SSBAs is mostly because their trigger patterns
are `content-irrelevant' and therefore act as `noises' for both humans and
DNNs. Motivated by this understanding, we propose to exploit content-relevant
features, $a.k.a.$ (human-relied) attributes, as the trigger patterns to design
clean-label SSBAs. This new attack paradigm is dubbed backdoor attack with
attribute trigger (BAAT). Extensive experiments are conducted on benchmark
datasets, which verify the effectiveness of our BAAT and its resistance to
existing defenses. | Cryptography and Security |
What field is the article from? | Title: Castor: Causal Temporal Regime Structure Learning
Abstract: The task of uncovering causal relationships among multivariate time series
data stands as an essential and challenging objective that cuts across a broad
array of disciplines ranging from climate science to healthcare. Such data
entails linear or non-linear relationships, and usually follow multiple a
priori unknown regimes. Existing causal discovery methods can infer summary
causal graphs from heterogeneous data with known regimes, but they fall short
in comprehensively learning both regimes and the corresponding causal graph. In
this paper, we introduce CASTOR, a novel framework designed to learn causal
relationships in heterogeneous time series data composed of various regimes,
each governed by a distinct causal graph. Through the maximization of a score
function via the EM algorithm, CASTOR infers the number of regimes and learns
linear or non-linear causal relationships in each regime. We demonstrate the
robust convergence properties of CASTOR, specifically highlighting its
proficiency in accurately identifying unique regimes. Empirical evidence,
garnered from exhaustive synthetic experiments and two real-world benchmarks,
confirm CASTOR's superior performance in causal discovery compared to baseline
methods. By learning a full temporal causal graph for each regime, CASTOR
establishes itself as a distinctly interpretable method for causal discovery in
heterogeneous time series. | Machine Learning |
What field is the article from? | Title: HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
Abstract: Graphs have emerged as a natural choice to represent and analyze the
intricate patterns and rich information of the Web, enabling applications such
as online page classification and social recommendation. The prevailing
"pre-train, fine-tune" paradigm has been widely adopted in graph machine
learning tasks, particularly in scenarios with limited labeled nodes. However,
this approach often exhibits a misalignment between the training objectives of
pretext tasks and those of downstream tasks. This gap can result in the
"negative transfer" problem, wherein the knowledge gained from pre-training
adversely affects performance in the downstream tasks. The surge in
prompt-based learning within Natural Language Processing (NLP) suggests the
potential of adapting a "pre-train, prompt" paradigm to graphs as an
alternative. However, existing graph prompting techniques are tailored to
homogeneous graphs, neglecting the inherent heterogeneity of Web graphs. To
bridge this gap, we propose HetGPT, a general post-training prompting framework
to improve the predictive performance of pre-trained heterogeneous graph neural
networks (HGNNs). The key is the design of a novel prompting function that
integrates a virtual class prompt and a heterogeneous feature prompt, with the
aim to reformulate downstream tasks to mirror pretext tasks. Moreover, HetGPT
introduces a multi-view neighborhood aggregation mechanism, capturing the
complex neighborhood structure in heterogeneous graphs. Extensive experiments
on three benchmark datasets demonstrate HetGPT's capability to enhance the
performance of state-of-the-art HGNNs on semi-supervised node classification. | Machine Learning |
What field is the article from? | Title: Cooperative AI via Decentralized Commitment Devices
Abstract: Credible commitment devices have been a popular approach for robust
multi-agent coordination. However, existing commitment mechanisms face
limitations like privacy, integrity, and susceptibility to mediator or user
strategic behavior. It is unclear if the cooperative AI techniques we study are
robust to real-world incentives and attack vectors. However, decentralized
commitment devices that utilize cryptography have been deployed in the wild,
and numerous studies have shown their ability to coordinate algorithmic agents
facing adversarial opponents with significant economic incentives, currently in
the order of several million to billions of dollars. In this paper, we use
examples in the decentralization and, in particular, Maximal Extractable Value
(MEV) (arXiv:1904.05234) literature to illustrate the potential security issues
in cooperative AI. We call for expanded research into decentralized commitments
to advance cooperative AI capabilities for secure coordination in open
environments and empirical testing frameworks to evaluate multi-agent
coordination ability given real-world commitment constraints. | Artificial Intelligence |
What field is the article from? | Title: Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions
Abstract: Lung cancer is responsible for 21% of cancer deaths in the UK and five-year
survival rates are heavily influenced by the stage the cancer was identified
at. Recent studies have demonstrated the capability of AI methods for accurate
and early diagnosis of lung cancer from routine scans. However, this evidence
has not translated into clinical practice with one barrier being a lack of
interpretable models. This study investigates the application Variational
Autoencoders (VAEs), a type of generative AI model, to lung cancer lesions.
Proposed models were trained on lesions extracted from 3D CT scans in the
LIDC-IDRI public dataset. Latent vector representations of 2D slices produced
by the VAEs were explored through clustering to justify their quality and used
in an MLP classifier model for lung cancer diagnosis, the best model achieved
state-of-the-art metrics of AUC 0.98 and 93.1% accuracy. Cluster analysis shows
the VAE latent space separates the dataset of malignant and benign lesions
based on meaningful feature components including tumour size, shape, patient
and malignancy class. We also include a comparative analysis of the standard
Gaussian VAE (GVAE) and the more recent Dirichlet VAE (DirVAE), which replaces
the prior with a Dirichlet distribution to encourage a more explainable latent
space with disentangled feature representation. Finally, we demonstrate the
potential for latent space traversals corresponding to clinically meaningful
feature changes. | Computer Vision |
What field is the article from? | Title: Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction
Abstract: Continuous-time long-term event prediction plays an important role in many
application scenarios. Most existing works rely on autoregressive frameworks to
predict event sequences, which suffer from error accumulation, thus
compromising prediction quality. Inspired by the success of denoising diffusion
probabilistic models, we propose a diffusion-based non-autoregressive temporal
point process model for long-term event prediction in continuous time. Instead
of generating events one at a time in an autoregressive way, our model predicts
the future event sequence entirely as a whole. In order to perform diffusion
processes on event sequences, we develop a bidirectional map between target
event sequences and the Euclidean vector space. Furthermore, we design a novel
denoising network to capture both sequential and contextual features for better
sample quality. Extensive experiments are conducted to prove the superiority of
our proposed model over state-of-the-art methods on long-term event prediction
in continuous time. To the best of our knowledge, this is the first work to
apply diffusion methods to long-term event prediction problems. | Machine Learning |
What field is the article from? | Title: Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images
Abstract: Recent advances in generative deep learning have enabled the creation of
high-quality synthetic images in text-to-image generation. Prior work shows
that fine-tuning a pretrained diffusion model on ImageNet and generating
synthetic training images from the finetuned model can enhance an ImageNet
classifier's performance. However, performance degrades as synthetic images
outnumber real ones. In this paper, we explore whether generative fine-tuning
is essential for this improvement and whether it is possible to further scale
up training using more synthetic data. We present a new framework leveraging
off-the-shelf generative models to generate synthetic training images,
addressing multiple challenges: class name ambiguity, lack of diversity in
naive prompts, and domain shifts. Specifically, we leverage large language
models (LLMs) and CLIP to resolve class name ambiguity. To diversify images, we
propose contextualized diversification (CD) and stylized diversification (SD)
methods, also prompted by LLMs. Finally, to mitigate domain shifts, we leverage
domain adaptation techniques with auxiliary batch normalization for synthetic
images. Our framework consistently enhances recognition model performance with
more synthetic data, up to 6x of original ImageNet size showcasing the
potential of synthetic data for improved recognition models and strong
out-of-domain generalization. | Computer Vision |
What field is the article from? | Title: Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis
Abstract: Game theory, as an analytical tool, is frequently utilized to analyze human
behavior in social science research. With the high alignment between the
behavior of Large Language Models (LLMs) and humans, a promising research
direction is to employ LLMs as substitutes for humans in game experiments,
enabling social science research. However, despite numerous empirical
researches on the combination of LLMs and game theory, the capability
boundaries of LLMs in game theory remain unclear. In this research, we endeavor
to systematically analyze LLMs in the context of game theory. Specifically,
rationality, as the fundamental principle of game theory, serves as the metric
for evaluating players' behavior -- building a clear desire, refining belief
about uncertainty, and taking optimal actions. Accordingly, we select three
classical games (dictator game, Rock-Paper-Scissors, and ring-network game) to
analyze to what extent LLMs can achieve rationality in these three aspects. The
experimental results indicate that even the current state-of-the-art LLM
(GPT-4) exhibits substantial disparities compared to humans in game theory. For
instance, LLMs struggle to build desires based on uncommon preferences, fail to
refine belief from many simple patterns, and may overlook or modify refined
belief when taking actions. Therefore, we consider that introducing LLMs into
game experiments in the field of social science should be approached with
greater caution. | Artificial Intelligence |
What field is the article from? | Title: Cone Ranking for Multi-Criteria Decision Making
Abstract: Recently introduced cone distribution functions from statistics are turned
into multi-criteria decision making (MCDM) tools. It is demonstrated that this
procedure can be considered as an upgrade of the weighted sum scalarization
insofar as it absorbs a whole collection of weighted sum scalarizations at once
instead of fixing a particular one in advance. Moreover, situations are
characterized in which different types of rank reversal occur, and it is
explained why this might even be useful for analyzing the ranking procedure. A
few examples will be discussed and a potential application in machine learning
is outlined. | Artificial Intelligence |
What field is the article from? | Title: Wired Perspectives: Multi-View Wire Art Embraces Generative AI
Abstract: Creating multi-view wire art (MVWA), a static 3D sculpture with diverse
interpretations from different viewpoints, is a complex task even for skilled
artists. In response, we present DreamWire, an AI system enabling everyone to
craft MVWA easily. Users express their vision through text prompts or
scribbles, freeing them from intricate 3D wire organisation. Our approach
synergises 3D B\'ezier curves, Prim's algorithm, and knowledge distillation
from diffusion models or their variants (e.g., ControlNet). This blend enables
the system to represent 3D wire art, ensuring spatial continuity and overcoming
data scarcity. Extensive evaluation and analysis are conducted to shed insight
on the inner workings of the proposed system, including the trade-off between
connectivity and visual aesthetics. | Computer Vision |
What field is the article from? | Title: Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons
Abstract: With the advancement of maritime unmanned aerial vehicles (UAVs) and deep
learning technologies, the application of UAV-based object detection has become
increasingly significant in the fields of maritime industry and ocean
engineering. Endowed with intelligent sensing capabilities, the maritime UAVs
enable effective and efficient maritime surveillance. To further promote the
development of maritime UAV-based object detection, this paper provides a
comprehensive review of challenges, relative methods, and UAV aerial datasets.
Specifically, in this work, we first briefly summarize four challenges for
object detection on maritime UAVs, i.e., object feature diversity, device
limitation, maritime environment variability, and dataset scarcity. We then
focus on computational methods to improve maritime UAV-based object detection
performance in terms of scale-aware, small object detection, view-aware,
rotated object detection, lightweight methods, and others. Next, we review the
UAV aerial image/video datasets and propose a maritime UAV aerial dataset named
MS2ship for ship detection. Furthermore, we conduct a series of experiments to
present the performance evaluation and robustness analysis of object detection
methods on maritime datasets. Eventually, we give the discussion and outlook on
future works for maritime UAV-based object detection. The MS2ship dataset is
available at
\href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}. | Computer Vision |
What field is the article from? | Title: Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency
Abstract: Previous entity disambiguation (ED) methods adopt a discriminative paradigm,
where prediction is made based on matching scores between mention context and
candidate entities using length-limited encoders. However, these methods often
struggle to capture explicit discourse-level dependencies, resulting in
incoherent predictions at the abstract level (e.g. topic or category). We
propose CoherentED, an ED system equipped with novel designs aimed at enhancing
the coherence of entity predictions. Our method first introduces an
unsupervised variational autoencoder (VAE) to extract latent topic vectors of
context sentences. This approach not only allows the encoder to handle longer
documents more effectively, conserves valuable input space, but also keeps a
topic-level coherence. Additionally, we incorporate an external category
memory, enabling the system to retrieve relevant categories for undecided
mentions. By employing step-by-step entity decisions, this design facilitates
the modeling of entity-entity interactions, thereby maintaining maximum
coherence at the category level. We achieve new state-of-the-art results on
popular ED benchmarks, with an average improvement of 1.3 F1 points. Our model
demonstrates particularly outstanding performance on challenging long-text
scenarios. | Computational Linguistics |
What field is the article from? | Title: Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning
Abstract: Recent advancements in motion planning for Autonomous Vehicles (AVs) show
great promise in using expert driver behaviors in non-stationary driving
environments. However, learning only through expert drivers needs more
generalizability to recover from domain shifts and near-failure scenarios due
to the dynamic behavior of traffic participants and weather conditions. A deep
Graph-based Prediction and Planning Policy Network (GP3Net) framework is
proposed for non-stationary environments that encodes the interactions between
traffic participants with contextual information and provides a decision for
safe maneuver for AV. A spatio-temporal graph models the interactions between
traffic participants for predicting the future trajectories of those
participants. The predicted trajectories are utilized to generate a future
occupancy map around the AV with uncertainties embedded to anticipate the
evolving non-stationary driving environments. Then the contextual information
and future occupancy maps are input to the policy network of the GP3Net
framework and trained using Proximal Policy Optimization (PPO) algorithm. The
proposed GP3Net performance is evaluated on standard CARLA benchmarking
scenarios with domain shifts of traffic patterns (urban, highway, and mixed).
The results show that the GP3Net outperforms previous state-of-the-art
imitation learning-based planning models for different towns. Further, in
unseen new weather conditions, GP3Net completes the desired route with fewer
traffic infractions. Finally, the results emphasize the advantage of including
the prediction module to enhance safety measures in non-stationary
environments. | Artificial Intelligence |
What field is the article from? | Title: DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text
Abstract: In this paper, we delineate the strategy employed by our team,
DeepLearningBrasil, which secured us the first place in the shared task
DepSign-LT-EDI@RANLP-2023, achieving a 47.0% Macro F1-Score and a notable 2.4%
advantage. The task was to classify social media texts into three distinct
levels of depression - "not depressed," "moderately depressed," and "severely
depressed." Leveraging the power of the RoBERTa and DeBERTa models, we further
pre-trained them on a collected Reddit dataset, specifically curated from
mental health-related Reddit's communities (Subreddits), leading to an enhanced
understanding of nuanced mental health discourse. To address lengthy textual
data, we used truncation techniques that retained the essence of the content by
focusing on its beginnings and endings. Our model was robust against unbalanced
data by incorporating sample weights into the loss. Cross-validation and
ensemble techniques were then employed to combine our k-fold trained models,
delivering an optimal solution. The accompanying code is made available for
transparency and further development. | Computational Linguistics |
What field is the article from? | Title: UniTeam: Open Vocabulary Mobile Manipulation Challenge
Abstract: This report introduces our UniTeam agent - an improved baseline for the
"HomeRobot: Open Vocabulary Mobile Manipulation" challenge. The challenge poses
problems of navigation in unfamiliar environments, manipulation of novel
objects, and recognition of open-vocabulary object classes. This challenge aims
to facilitate cross-cutting research in embodied AI using recent advances in
machine learning, computer vision, natural language, and robotics. In this
work, we conducted an exhaustive evaluation of the provided baseline agent;
identified deficiencies in perception, navigation, and manipulation skills; and
improved the baseline agent's performance. Notably, enhancements were made in
perception - minimizing misclassifications; navigation - preventing infinite
loop commitments; picking - addressing failures due to changing object
visibility; and placing - ensuring accurate positioning for successful object
placement. | Robotics |
What field is the article from? | Title: IG Captioner: Information Gain Captioners are Strong Zero-shot Classifiers
Abstract: Generative training has been demonstrated to be powerful for building
visual-language models. However, on zero-shot discriminative benchmarks, there
is still a performance gap between models trained with generative and
discriminative objectives. In this paper, we aim to narrow this gap by
improving the efficacy of generative training on classification tasks, without
any finetuning processes or additional modules.
Specifically, we focus on narrowing the gap between the generative captioner
and the CLIP classifier. We begin by analysing the predictions made by the
captioner and classifier and observe that the caption generation inherits the
distribution bias from the language model trained with pure text modality,
making it less grounded on the visual signal. To tackle this problem, we
redesign the scoring objective for the captioner to alleviate the
distributional bias and focus on measuring the gain of information brought by
the visual inputs. We further design a generative training objective to match
the evaluation objective. We name our model trained and evaluated from the
novel procedures as Information Gain (IG) captioner. We pretrain the models on
the public Laion-5B dataset and perform a series of discriminative evaluations.
For the zero-shot classification on ImageNet, IG captioner achieves $> 18\%$
improvements over the standard captioner, achieving comparable performances
with the CLIP classifier. IG captioner also demonstrated strong performance on
zero-shot image-text retrieval tasks on MSCOCO and Flickr30K. We hope this
paper inspires further research towards unifying generative and discriminative
training procedures for visual-language models. | Computer Vision |
What field is the article from? | Title: WAVER: Writing-style Agnostic Video Retrieval via Distilling Vision-Language Models Through Open-Vocabulary Knowledge
Abstract: Text-video retrieval, a prominent sub-field within the broader domain of
multimedia content management, has witnessed remarkable growth and innovation
over the past decade. However, existing methods assume the video scenes are
consistent and the description annotators are unbiased. These limitations fail
to align with fluid real-world scenarios, and descriptions can be influenced by
annotator biases, diverse writing styles, and varying textual perspectives. To
overcome the aforementioned problems, we introduce WAVER, a cross-domain
knowledge distillation mechanism designed to tackle the challenge of handling
writing-style agnostics. WAVER capitalizes on the open-vocabulary properties
inherent in pre-trained vision-language models and employs an implicit
knowledge distillation approach to transfer text-based knowledge from a teacher
model to a vision-based student. Empirical studies conducted across four
standard benchmark datasets, encompassing various settings, provide compelling
evidence that \WAVER can achieve state-of-the-art performance in text-video
retrieval tasks while handling writing-style variations. | Computer Vision |
What field is the article from? | Title: Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
Abstract: Language models have shown promise in various tasks but can be affected by
undesired data during training, fine-tuning, or alignment. For example, if some
unsafe conversations are wrongly annotated as safe ones, the model fine-tuned
on these samples may be harmful. Therefore, the correctness of annotations,
i.e., the credibility of the dataset, is important. This study focuses on the
credibility of real-world datasets, including the popular benchmarks Jigsaw
Civil Comments, Anthropic Harmless & Red Team, PKU BeaverTails & SafeRLHF, that
can be used for training a harmless language model. Given the cost and
difficulty of cleaning these datasets by humans, we introduce a systematic
framework for evaluating the credibility of datasets, identifying label errors,
and evaluating the influence of noisy labels in the curated language data,
specifically focusing on unsafe comments and conversation classification. With
the framework, we find and fix an average of 6.16% label errors in 11 datasets
constructed from the above benchmarks. The data credibility and downstream
learning performance can be remarkably improved by directly fixing label
errors, indicating the significance of cleaning existing real-world datasets.
Open-source: https://github.com/Docta-ai/docta. | Machine Learning |
What field is the article from? | Title: The Case for Scalable, Data-Driven Theory: A Paradigm for Scientific Progress in NLP
Abstract: I propose a paradigm for scientific progress in NLP centered around
developing scalable, data-driven theories of linguistic structure. The idea is
to collect data in tightly scoped, carefully defined ways which allow for
exhaustive annotation of behavioral phenomena of interest, and then use machine
learning to construct explanatory theories of these phenomena which can form
building blocks for intelligible AI systems. After laying some conceptual
groundwork, I describe several investigations into data-driven theories of
shallow semantic structure using Question-Answer driven Semantic Role Labeling
(QA-SRL), a schema for annotating verbal predicate-argument relations using
highly constrained question-answer pairs. While this only scratches the surface
of the complex language behaviors of interest in AI, I outline principles for
data collection and theoretical modeling which can inform future scientific
progress. This note summarizes and draws heavily on my PhD thesis. | Computational Linguistics |
What field is the article from? | Title: The Hidden Linear Structure in Score-Based Models and its Application
Abstract: Score-based models have achieved remarkable results in the generative
modeling of many domains. By learning the gradient of smoothed data
distribution, they can iteratively generate samples from complex distribution
e.g. natural images.
However, is there any universal structure in the gradient field that will
eventually be learned by any neural network? Here, we aim to find such
structures through a normative analysis of the score function.
First, we derived the closed-form solution to the scored-based model with a
Gaussian score. We claimed that for well-trained diffusion models, the learned
score at a high noise scale is well approximated by the linear score of
Gaussian. We demonstrated this through empirical validation of pre-trained
images diffusion model and theoretical analysis of the score function. This
finding enabled us to precisely predict the initial diffusion trajectory using
the analytical solution and to accelerate image sampling by 15-30\% by skipping
the initial phase without sacrificing image quality. Our finding of the linear
structure in the score-based model has implications for better model design and
data pre-processing. | Artificial Intelligence |
What field is the article from? | Title: Multi Time Scale World Models
Abstract: Intelligent agents use internal world models to reason and make predictions
about different courses of their actions at many scales. Devising learning
paradigms and architectures that allow machines to learn world models that
operate at multiple levels of temporal abstractions while dealing with complex
uncertainty predictions is a major technical hurdle. In this work, we propose a
probabilistic formalism to learn multi-time scale world models which we call
the Multi Time Scale State Space (MTS3) model. Our model uses a computationally
efficient inference scheme on multiple time scales for highly accurate
long-horizon predictions and uncertainty estimates over several seconds into
the future. Our experiments, which focus on action conditional long horizon
future predictions, show that MTS3 outperforms recent methods on several system
identification benchmarks including complex simulated and real-world dynamical
systems. Code is available at this repository: https://github.com/ALRhub/MTS3. | Machine Learning |
What field is the article from? | Title: An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach
Abstract: Phishing and spam detection is long standing challenge that has been the
subject of much academic research. Large Language Models (LLM) have vast
potential to transform society and provide new and innovative approaches to
solve well-established challenges. Phishing and spam have caused financial
hardships and lost time and resources to email users all over the world and
frequently serve as an entry point for ransomware threat actors. While
detection approaches exist, especially heuristic-based approaches, LLMs offer
the potential to venture into a new unexplored area for understanding and
solving this challenge. LLMs have rapidly altered the landscape from business,
consumers, and throughout academia and demonstrate transformational potential
for the potential of society. Based on this, applying these new and innovative
approaches to email detection is a rational next step in academic research. In
this work, we present IPSDM, our model based on fine-tuning the BERT family of
models to specifically detect phishing and spam email. We demonstrate our
fine-tuned version, IPSDM, is able to better classify emails in both unbalanced
and balanced datasets. This work serves as an important first step towards
employing LLMs to improve the security of our information systems. | Computational Linguistics |
What field is the article from? | Title: SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation
Abstract: Data augmentation is a crucial component in training neural networks to
overcome the limitation imposed by data size, and several techniques have been
studied for time series. Although these techniques are effective in certain
tasks, they have yet to be generalized to time series benchmarks. We find that
current data augmentation techniques ruin the core information contained within
the frequency domain. To address this issue, we propose a simple strategy to
preserve spectral information (SimPSI) in time series data augmentation. SimPSI
preserves the spectral information by mixing the original and augmented input
spectrum weighted by a preservation map, which indicates the importance score
of each frequency. Specifically, our experimental contributions are to build
three distinct preservation maps: magnitude spectrum, saliency map, and
spectrum-preservative map. We apply SimPSI to various time series data
augmentations and evaluate its effectiveness across a wide range of time series
benchmarks. Our experimental results support that SimPSI considerably enhances
the performance of time series data augmentations by preserving core spectral
information. The source code used in the paper is available at
https://github.com/Hyun-Ryu/simpsi. | Machine Learning |
What field is the article from? | Title: Sequential Planning in Large Partially Observable Environments guided by LLMs
Abstract: Sequential planning in large state space and action space quickly becomes
intractable due to combinatorial explosion of the search space. Heuristic
methods, like monte-carlo tree search, though effective for large state space,
but struggle if action space is large. Pure reinforcement learning methods,
relying only on reward signals, needs prohibitively large interactions with the
environment to device a viable plan. If the state space, observations and
actions can be represented in natural language then Large Language models (LLM)
can be used to generate action plans. Recently several such goal-directed
agents like Reflexion, CLIN, SayCan were able to surpass the performance of
other state-of-the-art methods with minimum or no task specific training. But
they still struggle with exploration and get stuck in local optima. Their
planning capabilities are limited by the limited reasoning capability of the
foundational LLMs on text data. We propose a hybrid agent "neoplanner", that
synergizes both state space search with queries to foundational LLM to get the
best action plan. The reward signals are quantitatively used to drive the
search. A balance of exploration and exploitation is maintained by maximizing
upper confidence bounds of values of states. In places where random exploration
is needed, the LLM is queried to generate an action plan. Learnings from each
trial are stored as entity relationships in text format. Those are used in
future queries to the LLM for continual improvement. Experiments in the
Scienceworld environment reveals a 124% improvement from the current best
method in terms of average reward gained across multiple tasks. | Artificial Intelligence |
What field is the article from? | Title: Quantum learning and essential cognition under the traction of meta-characteristics in an open world
Abstract: Artificial intelligence has made significant progress in the Close World
problem, being able to accurately recognize old knowledge through training and
classification. However, AI faces significant challenges in the Open World
problem, as it involves a new and unknown exploration journey. AI is not
inherently proactive in exploration, and its challenge lies in not knowing how
to approach and adapt to the unknown world. How do humans acquire knowledge of
the unknown world. Humans identify new knowledge through intrinsic cognition.
In the process of recognizing new colors, the cognitive cues are different from
known color features and involve hue, saturation, brightness, and other
characteristics. When AI encounters objects with different features in the new
world, it faces another challenge: where are the distinguishing features
between influential features of new and old objects? AI often mistakes a new
world's brown bear for a known dog because it has not learned the differences
in feature distributions between knowledge systems. This is because things in
the new and old worlds have different units and dimensions for their features.
This paper proposes an open-world model and elemental feature system that
focuses on fundamentally recognizing the distribution differences in objective
features between the new and old worlds. The quantum tunneling effect of
learning ability in the new and old worlds is realized through the tractive
force of meta-characteristic. The outstanding performance of the model system
in learning new knowledge (using pedestrian re-identification datasets as an
example) demonstrates that AI has acquired the ability to recognize the new
world with an accuracy of $96.71\%$ at most and has gained the capability to
explore new knowledge, similar to humans. | Artificial Intelligence |
What field is the article from? | Title: Evaluative Item-Contrastive Explanations in Rankings
Abstract: The remarkable success of Artificial Intelligence in advancing automated
decision-making is evident both in academia and industry. Within the plethora
of applications, ranking systems hold significant importance in various
domains. This paper advocates for the application of a specific form of
Explainable AI -- namely, contrastive explanations -- as particularly
well-suited for addressing ranking problems. This approach is especially potent
when combined with an Evaluative AI methodology, which conscientiously
evaluates both positive and negative aspects influencing a potential ranking.
Therefore, the present work introduces Evaluative Item-Contrastive Explanations
tailored for ranking systems and illustrates its application and
characteristics through an experiment conducted on publicly available data. | Information Retrieval |
What field is the article from? | Title: SAGE: Smart home Agent with Grounded Execution
Abstract: This article introduces SAGE (Smart home Agent with Grounded Execution), a
framework designed to maximize the flexibility of smart home assistants by
replacing manually-defined inference logic with an LLM-powered autonomous agent
system. SAGE integrates information about user preferences, device states, and
external factors (such as weather and TV schedules) through the orchestration
of a collection of tools. SAGE's capabilities include learning user preferences
from natural-language utterances, interacting with devices by reading their API
documentation, writing code to continuously monitor devices, and understanding
natural device references. To evaluate SAGE, we develop a benchmark of 43
highly challenging smart home tasks, where SAGE successfully achieves 23 tasks,
significantly outperforming existing LLM-enabled baselines (5/43). | Artificial Intelligence |
What field is the article from? | Title: Performance Trade-offs of Watermarking Large Language Models
Abstract: Amidst growing concerns of large language models (LLMs) being misused for
generating misinformation or completing homework assignments, watermarking has
emerged as an effective solution for distinguishing human-written and
LLM-generated text. A prominent watermarking strategy is to embed a signal into
generated text by upsampling a (pseudorandomly-chosen) subset of tokens at
every generation step. Although this signal is imperceptible to a human reader,
it is detectable through statistical testing. However, implanting such signals
alters the model's output distribution and can have unintended effects when
watermarked LLMs are used for downstream applications. In this work, we
evaluate the performance of watermarked LLMs on a diverse suite of tasks,
including text classification, textual entailment, reasoning, question
answering, translation, summarization, and language modeling. We find that
watermarking has negligible impact on the performance of tasks posed as k-class
classification problems in the average case. However, the accuracy can plummet
to that of a random classifier for some scenarios (that occur with
non-negligible probability). Tasks that are cast as multiple-choice questions
and short-form generation are surprisingly unaffected by watermarking. For
long-form generation tasks, including summarization and translation, we see a
drop of 15-20% in the performance due to watermarking. Our findings highlight
the trade-offs that users should be cognizant of when using watermarked models,
and point to cases where future research could improve existing trade-offs. | Computational Linguistics |
What field is the article from? | Title: VideoLCM: Video Latent Consistency Model
Abstract: Consistency models have demonstrated powerful capability in efficient image
generation and allowed synthesis within a few sampling steps, alleviating the
high computational cost in diffusion models. However, the consistency model in
the more challenging and resource-consuming video generation is still less
explored. In this report, we present the VideoLCM framework to fill this gap,
which leverages the concept of consistency models from image generation to
efficiently synthesize videos with minimal steps while maintaining high
quality. VideoLCM builds upon existing latent video diffusion models and
incorporates consistency distillation techniques for training the latent
consistency model. Experimental results reveal the effectiveness of our
VideoLCM in terms of computational efficiency, fidelity and temporal
consistency. Notably, VideoLCM achieves high-fidelity and smooth video
synthesis with only four sampling steps, showcasing the potential for real-time
synthesis. We hope that VideoLCM can serve as a simple yet effective baseline
for subsequent research. The source code and models will be publicly available. | Computer Vision |
What field is the article from? | Title: Transforming organic chemistry research paradigms: moving from manual efforts to the intersection of automation and artificial intelligence
Abstract: Organic chemistry is undergoing a major paradigm shift, moving from a
labor-intensive approach to a new era dominated by automation and artificial
intelligence (AI). This transformative shift is being driven by technological
advances, the ever-increasing demand for greater research efficiency and
accuracy, and the burgeoning growth of interdisciplinary research. AI models,
supported by computational power and algorithms, are drastically reshaping
synthetic planning and introducing groundbreaking ways to tackle complex
molecular synthesis. In addition, autonomous robotic systems are rapidly
accelerating the pace of discovery by performing tedious tasks with
unprecedented speed and precision. This article examines the multiple
opportunities and challenges presented by this paradigm shift and explores its
far-reaching implications. It provides valuable insights into the future
trajectory of organic chemistry research, which is increasingly defined by the
synergistic interaction of automation and AI. | Artificial Intelligence |
What field is the article from? | Title: Career Path Prediction using Resume Representation Learning and Skill-based Matching
Abstract: The impact of person-job fit on job satisfaction and performance is widely
acknowledged, which highlights the importance of providing workers with next
steps at the right time in their career. This task of predicting the next step
in a career is known as career path prediction, and has diverse applications
such as turnover prevention and internal job mobility. Existing methods to
career path prediction rely on large amounts of private career history data to
model the interactions between job titles and companies. We propose leveraging
the unexplored textual descriptions that are part of work experience sections
in resumes. We introduce a structured dataset of 2,164 anonymized career
histories, annotated with ESCO occupation labels. Based on this dataset, we
present a novel representation learning approach, CareerBERT, specifically
designed for work history data. We develop a skill-based model and a text-based
model for career path prediction, which achieve 35.24% and 39.61% recall@10
respectively on our dataset. Finally, we show that both approaches are
complementary as a hybrid approach achieves the strongest result with 43.01%
recall@10. | Computational Linguistics |
What field is the article from? | Title: A Foundational Framework and Methodology for Personalized Early and Timely Diagnosis
Abstract: Early diagnosis of diseases holds the potential for deep transformation in
healthcare by enabling better treatment options, improving long-term survival
and quality of life, and reducing overall cost. With the advent of medical big
data, advances in diagnostic tests as well as in machine learning and
statistics, early or timely diagnosis seems within reach. Early diagnosis
research often neglects the potential for optimizing individual diagnostic
paths. To enable personalized early diagnosis, a foundational framework is
needed that delineates the diagnosis process and systematically identifies the
time-dependent value of various diagnostic tests for an individual patient
given their unique characteristics. Here, we propose the first foundational
framework for early and timely diagnosis. It builds on decision-theoretic
approaches to outline the diagnosis process and integrates machine learning and
statistical methodology for estimating the optimal personalized diagnostic
path. To describe the proposed framework as well as possibly other frameworks,
we provide essential definitions.
The development of a foundational framework is necessary for several reasons:
1) formalism provides clarity for the development of decision support tools; 2)
observed information can be complemented with estimates of the future patient
trajectory; 3) the net benefit of counterfactual diagnostic paths and
associated uncertainties can be modeled for individuals 4) 'early' and 'timely'
diagnosis can be clearly defined; 5) a mechanism emerges for assessing the
value of technologies in terms of their impact on personalized early diagnosis,
resulting health outcomes and incurred costs.
Finally, we hope that this foundational framework will unlock the
long-awaited potential of timely diagnosis and intervention, leading to
improved outcomes for patients and higher cost-effectiveness for healthcare
systems. | Machine Learning |
What field is the article from? | Title: MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction
Abstract: Hospital readmission prediction is considered an essential approach to
decreasing readmission rates, which is a key factor in assessing the quality
and efficacy of a healthcare system. Previous studies have extensively utilized
three primary modalities, namely electronic health records (EHR), medical
images, and clinical notes, to predict hospital readmissions. However, the
majority of these studies did not integrate information from all three
modalities or utilize the spatiotemporal relationships present in the dataset.
This study introduces a novel model called the Multimodal Spatiotemporal
Graph-Transformer (MuST) for predicting hospital readmissions. By employing
Graph Convolution Networks and temporal transformers, we can effectively
capture spatial and temporal dependencies in EHR and chest radiographs. We then
propose a fusion transformer to combine the spatiotemporal features from the
two modalities mentioned above with the features from clinical notes extracted
by a pre-trained, domain-specific transformer. We assess the effectiveness of
our methods using the latest publicly available dataset, MIMIC-IV. The
experimental results indicate that the inclusion of multimodal features in MuST
improves its performance in comparison to unimodal methods. Furthermore, our
proposed pipeline outperforms the current leading methods in the prediction of
hospital readmissions. | Machine Learning |
What field is the article from? | Title: MetaSymNet: A Dynamic Symbolic Regression Network Capable of Evolving into Arbitrary Formulations
Abstract: Mathematical formulas serve as the means of communication between humans and
nature, encapsulating the operational laws governing natural phenomena. The
concise formulation of these laws is a crucial objective in scientific research
and an important challenge for artificial intelligence (AI). While traditional
artificial neural networks (MLP) excel at data fitting, they often yield
uninterpretable black box results that hinder our understanding of the
relationship between variables x and predicted values y. Moreover, the fixed
network architecture in MLP often gives rise to redundancy in both network
structure and parameters. To address these issues, we propose MetaSymNet, a
novel neural network that dynamically adjusts its structure in real-time,
allowing for both expansion and contraction. This adaptive network employs the
PANGU meta function as its activation function, which is a unique type capable
of evolving into various basic functions during training to compose
mathematical formulas tailored to specific needs. We then evolve the neural
network into a concise, interpretable mathematical expression. To evaluate
MetaSymNet's performance, we compare it with four state-of-the-art symbolic
regression algorithms across more than 10 public datasets comprising 222
formulas. Our experimental results demonstrate that our algorithm outperforms
others consistently regardless of noise presence or absence. Furthermore, we
assess MetaSymNet against MLP and SVM regarding their fitting ability and
extrapolation capability, these are two essential aspects of machine learning
algorithms. The findings reveal that our algorithm excels in both areas.
Finally, we compared MetaSymNet with MLP using iterative pruning in network
structure complexity. The results show that MetaSymNet's network structure
complexity is obviously less than MLP under the same goodness of fit. | Machine Learning |
What field is the article from? | Title: Aggregate, Decompose, and Fine-Tune: A Simple Yet Effective Factor-Tuning Method for Vision Transformer
Abstract: Recent advancements have illuminated the efficacy of some
tensorization-decomposition Parameter-Efficient Fine-Tuning methods like LoRA
and FacT in the context of Vision Transformers (ViT). However, these methods
grapple with the challenges of inadequately addressing inner- and cross-layer
redundancy. To tackle this issue, we introduce EFfective Factor-Tuning (EFFT),
a simple yet effective fine-tuning method. Within the VTAB-1K dataset, our EFFT
surpasses all baselines, attaining state-of-the-art performance with a
categorical average of 75.9% in top-1 accuracy with only 0.28% of the
parameters for full fine-tuning. Considering the simplicity and efficacy of
EFFT, it holds the potential to serve as a foundational benchmark. The code and
model are now available at
https://github.com/Dongping-Chen/EFFT-EFfective-Factor-Tuning. | Computer Vision |
What field is the article from? | Title: Privacy-Aware Document Visual Question Answering
Abstract: Document Visual Question Answering (DocVQA) is a fast growing branch of
document understanding. Despite the fact that documents contain sensitive or
copyrighted information, none of the current DocVQA methods offers strong
privacy guarantees.
In this work, we explore privacy in the domain of DocVQA for the first time.
We highlight privacy issues in state of the art multi-modal LLM models used for
DocVQA, and explore possible solutions.
Specifically, we focus on the invoice processing use case as a realistic,
widely used scenario for document understanding, and propose a large scale
DocVQA dataset comprising invoice documents and associated questions and
answers. We employ a federated learning scheme, that reflects the real-life
distribution of documents in different businesses, and we explore the use case
where the ID of the invoice issuer is the sensitive information to be
protected.
We demonstrate that non-private models tend to memorise, behaviour that can
lead to exposing private information. We then evaluate baseline training
schemes employing federated learning and differential privacy in this
multi-modal scenario, where the sensitive information might be exposed through
any of the two input modalities: vision (document image) or language (OCR
tokens).
Finally, we design an attack exploiting the memorisation effect of the model,
and demonstrate its effectiveness in probing different DocVQA models. | Computer Vision |
What field is the article from? | Title: New Epochs in AI Supervision: Design and Implementation of an Autonomous Radiology AI Monitoring System
Abstract: With the increasingly widespread adoption of AI in healthcare, maintaining
the accuracy and reliability of AI models in clinical practice has become
crucial. In this context, we introduce novel methods for monitoring the
performance of radiology AI classification models in practice, addressing the
challenges of obtaining real-time ground truth for performance monitoring. We
propose two metrics - predictive divergence and temporal stability - to be used
for preemptive alerts of AI performance changes. Predictive divergence,
measured using Kullback-Leibler and Jensen-Shannon divergences, evaluates model
accuracy by comparing predictions with those of two supplementary models.
Temporal stability is assessed through a comparison of current predictions
against historical moving averages, identifying potential model decay or data
drift. This approach was retrospectively validated using chest X-ray data from
a single-center imaging clinic, demonstrating its effectiveness in maintaining
AI model reliability. By providing continuous, real-time insights into model
performance, our system ensures the safe and effective use of AI in clinical
decision-making, paving the way for more robust AI integration in healthcare | Artificial Intelligence |
What field is the article from? | Title: Linear Mode Connectivity in Sparse Neural Networks
Abstract: With the rise in interest of sparse neural networks, we study how neural
network pruning with synthetic data leads to sparse networks with unique
training properties. We find that distilled data, a synthetic summarization of
the real data, paired with Iterative Magnitude Pruning (IMP) unveils a new
class of sparse networks that are more stable to SGD noise on the real data,
than either the dense model, or subnetworks found with real data in IMP. That
is, synthetically chosen subnetworks often train to the same minima, or exhibit
linear mode connectivity. We study this through linear interpolation, loss
landscape visualizations, and measuring the diagonal of the hessian. While
dataset distillation as a field is still young, we find that these properties
lead to synthetic subnetworks matching the performance of traditional IMP with
up to 150x less training points in settings where distilled data applies. | Machine Learning |
What field is the article from? | Title: Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection
Abstract: Video Moment Retrieval (MR) and Highlight Detection (HD) have attracted
significant attention due to the growing demand for video analysis. Recent
approaches treat MR and HD as similar video grounding problems and address them
together with transformer-based architecture. However, we observe that the
emphasis of MR and HD differs, with one necessitating the perception of local
relationships and the other prioritizing the understanding of global contexts.
Consequently, the lack of task-specific design will inevitably lead to
limitations in associating the intrinsic specialty of two tasks. To tackle the
issue, we propose a Unified Video COMprehension framework (UVCOM) to bridge the
gap and jointly solve MR and HD effectively. By performing progressive
integration on intra and inter-modality across multi-granularity, UVCOM
achieves the comprehensive understanding in processing a video. Moreover, we
present multi-aspect contrastive learning to consolidate the local relation
modeling and global knowledge accumulation via well aligned multi-modal space.
Extensive experiments on QVHighlights, Charades-STA, TACoS , YouTube Highlights
and TVSum datasets demonstrate the effectiveness and rationality of UVCOM which
outperforms the state-of-the-art methods by a remarkable margin. | Computer Vision |
What field is the article from? | Title: User Persona Identification and New Service Adaptation Recommendation
Abstract: Providing a personalized user experience on information dense webpages helps
users in reaching their end-goals sooner. We explore an automated approach to
identifying user personas by leveraging high dimensional trajectory information
from user sessions on webpages. While neural collaborative filtering (NCF)
approaches pay little attention to token semantics, our method introduces
SessionBERT, a Transformer-backed language model trained from scratch on the
masked language modeling (mlm) objective for user trajectories (pages,
metadata, billing in a session) aiming to capture semantics within them. Our
results show that representations learned through SessionBERT are able to
consistently outperform a BERT-base model providing a 3% and 1% relative
improvement in F1-score for predicting page links and next services. We
leverage SessionBERT and extend it to provide recommendations (top-5) for the
next most-relevant services that a user would be likely to use. We achieve a
HIT@5 of 58% from our recommendation model. | Information Retrieval |
What field is the article from? | Title: Towards Learning a Generalist Model for Embodied Navigation
Abstract: Building a generalist agent that can interact with the world is the
intriguing target of AI systems, thus spurring the research for embodied
navigation, where an agent is required to navigate according to instructions or
respond to queries. Despite the major progress attained, previous works
primarily focus on task-specific agents and lack generalizability to unseen
scenarios. Recently, LLMs have presented remarkable capabilities across various
fields, and provided a promising opportunity for embodied navigation. Drawing
on this, we propose the first generalist model for embodied navigation,
NaviLLM. It adapts LLMs to embodied navigation by introducing schema-based
instruction. The schema-based instruction flexibly casts various tasks into
generation problems, thereby unifying a wide range of tasks. This approach
allows us to integrate diverse data sources from various datasets into the
training, equipping NaviLLM with a wide range of capabilities required by
embodied navigation. We conduct extensive experiments to evaluate the
performance and generalizability of our model. The experimental results
demonstrate that our unified model achieves state-of-the-art performance on
CVDN, SOON, and ScanQA. Specifically, it surpasses the previous
stats-of-the-art method by a significant margin of 29% in goal progress on
CVDN. Moreover, our model also demonstrates strong generalizability and
presents impressive results on unseen tasks, e.g., embodied question answering
and 3D captioning. | Computer Vision |
What field is the article from? | Title: Automated Process Planning Based on a Semantic Capability Model and SMT
Abstract: In research of manufacturing systems and autonomous robots, the term
capability is used for a machine-interpretable specification of a system
function. Approaches in this research area develop information models that
capture all information relevant to interpret the requirements, effects and
behavior of functions. These approaches are intended to overcome the
heterogeneity resulting from the various types of processes and from the large
number of different vendors. However, these models and associated methods do
not offer solutions for automated process planning, i.e. finding a sequence of
individual capabilities required to manufacture a certain product or to
accomplish a mission using autonomous robots. Instead, this is a typical task
for AI planning approaches, which unfortunately require a high effort to create
the respective planning problem descriptions. In this paper, we present an
approach that combines these two topics: Starting from a semantic capability
model, an AI planning problem is automatically generated. The planning problem
is encoded using Satisfiability Modulo Theories and uses an existing solver to
find valid capability sequences including required parameter values. The
approach also offers possibilities to integrate existing human expertise and to
provide explanations for human operators in order to help understand planning
decisions. | Artificial Intelligence |
What field is the article from? | Title: Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network
Abstract: Ball mills play a critical role in modern mining operations, making their
bearing failures a significant concern due to the potential loss of production
efficiency and economic consequences. This paper presents an anomaly detection
method based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for
addressing the issue of ball mill bearing fault detection. The proposed
approach leverages vibration data collected during normal operation for
training, overcoming challenges such as labeling issues and data imbalance
often encountered in supervised learning methods. DCAN includes the modules of
convolutional feature extraction and transposed convolutional feature
reconstruction, demonstrating exceptional capabilities in signal processing and
feature extraction. Additionally, the paper describes the practical deployment
of the DCAN-based anomaly detection model for bearing fault detection,
utilizing data from the ball mill bearings of Wuhan Iron & Steel Resources
Group and fault data from NASA's bearing vibration dataset. Experimental
results validate the DCAN model's reliability in recognizing fault vibration
patterns. This method holds promise for enhancing bearing fault detection
efficiency, reducing production interruptions, and lowering maintenance costs. | Machine Learning |
What field is the article from? | Title: A density estimation perspective on learning from pairwise human preferences
Abstract: Learning from human feedback (LHF) -- and in particular learning from
pairwise preferences -- has recently become a crucial ingredient in training
large language models (LLMs), and has been the subject of much research. Most
recent works frame it as a reinforcement learning problem, where a reward
function is learned from pairwise preference data and the LLM is treated as a
policy which is adapted to maximize the rewards, often under additional
regularization constraints. We propose an alternative interpretation which
centers on the generative process for pairwise preferences and treats LHF as a
density estimation problem. We provide theoretical and empirical results
showing that for a family of generative processes defined via preference
behavior distribution equations, training a reward function on pairwise
preferences effectively models an annotator's implicit preference distribution.
Finally, we discuss and present findings on "annotator misspecification" --
failure cases where wrong modeling assumptions are made about annotator
behavior, resulting in poorly-adapted models -- suggesting that approaches that
learn from pairwise human preferences could have trouble learning from a
population of annotators with diverse viewpoints. | Machine Learning |
What field is the article from? | Title: Deep Tensor Network
Abstract: In this paper, we delve into the foundational principles of tensor
categories, harnessing the universal property of the tensor product to pioneer
novel methodologies in deep network architectures. Our primary contribution is
the introduction of the Tensor Attention and Tensor Interaction Mechanism, a
groundbreaking approach that leverages the tensor category to enhance the
computational efficiency and the expressiveness of deep networks, and can even
be generalized into the quantum realm. | Machine Learning |
What field is the article from? | Title: On The Relationship Between Universal Adversarial Attacks And Sparse Representations
Abstract: The prominent success of neural networks, mainly in computer vision tasks, is
increasingly shadowed by their sensitivity to small, barely perceivable
adversarial perturbations in image input.
In this work, we aim at explaining this vulnerability through the framework
of sparsity.
We show the connection between adversarial attacks and sparse
representations, with a focus on explaining the universality and
transferability of adversarial examples in neural networks.
To this end, we show that sparse coding algorithms, and the neural
network-based learned iterative shrinkage thresholding algorithm (LISTA) among
them, suffer from this sensitivity, and that common attacks on neural networks
can be expressed as attacks on the sparse representation of the input image.
The phenomenon that we observe holds true also when the network is agnostic to
the sparse representation and dictionary, and thus can provide a possible
explanation for the universality and transferability of adversarial attacks.
The code is available at
https://github.com/danawr/adversarial_attacks_and_sparse_representations. | Computer Vision |
What field is the article from? | Title: Multicoated and Folded Graph Neural Networks with Strong Lottery Tickets
Abstract: The Strong Lottery Ticket Hypothesis (SLTH) demonstrates the existence of
high-performing subnetworks within a randomly initialized model, discoverable
through pruning a convolutional neural network (CNN) without any weight
training. A recent study, called Untrained GNNs Tickets (UGT), expanded SLTH
from CNNs to shallow graph neural networks (GNNs). However, discrepancies
persist when comparing baseline models with learned dense weights.
Additionally, there remains an unexplored area in applying SLTH to deeper GNNs,
which, despite delivering improved accuracy with additional layers, suffer from
excessive memory requirements. To address these challenges, this work utilizes
Multicoated Supermasks (M-Sup), a scalar pruning mask method, and implements it
in GNNs by proposing a strategy for setting its pruning thresholds adaptively.
In the context of deep GNNs, this research uncovers the existence of untrained
recurrent networks, which exhibit performance on par with their trained
feed-forward counterparts. This paper also introduces the Multi-Stage Folding
and Unshared Masks methods to expand the search space in terms of both
architecture and parameters. Through the evaluation of various datasets,
including the Open Graph Benchmark (OGB), this work establishes a triple-win
scenario for SLTH-based GNNs: by achieving high sparsity, competitive
performance, and high memory efficiency with up to 98.7\% reduction, it
demonstrates suitability for energy-efficient graph processing. | Machine Learning |
What field is the article from? | Title: MultiIoT: Towards Large-scale Multisensory Learning for the Internet of Things
Abstract: The Internet of Things (IoT), the network integrating billions of smart
physical devices embedded with sensors, software, and communication
technologies for the purpose of connecting and exchanging data with other
devices and systems, is a critical and rapidly expanding component of our
modern world. The IoT ecosystem provides a rich source of real-world modalities
such as motion, thermal, geolocation, imaging, depth, sensors, video, and audio
for prediction tasks involving the pose, gaze, activities, and gestures of
humans as well as the touch, contact, pose, 3D of physical objects. Machine
learning presents a rich opportunity to automatically process IoT data at
scale, enabling efficient inference for impact in understanding human
wellbeing, controlling physical devices, and interconnecting smart cities. To
develop machine learning technologies for IoT, this paper proposes MultiIoT,
the most expansive IoT benchmark to date, encompassing over 1.15 million
samples from 12 modalities and 8 tasks. MultiIoT introduces unique challenges
involving (1) learning from many sensory modalities, (2) fine-grained
interactions across long temporal ranges, and (3) extreme heterogeneity due to
unique structure and noise topologies in real-world sensors. We also release a
set of strong modeling baselines, spanning modality and task-specific methods
to multisensory and multitask models to encourage future research in
multisensory representation learning for IoT. | Machine Learning |
What field is the article from? | Title: Sparse Training of Discrete Diffusion Models for Graph Generation
Abstract: Generative models for graphs often encounter scalability challenges due to
the inherent need to predict interactions for every node pair. Despite the
sparsity often exhibited by real-world graphs, the unpredictable sparsity
patterns of their adjacency matrices, stemming from their unordered nature,
leads to quadratic computational complexity. In this work, we introduce
SparseDiff, a denoising diffusion model for graph generation that is able to
exploit sparsity during its training phase. At the core of SparseDiff is a
message-passing neural network tailored to predict only a subset of edges
during each forward pass. When combined with a sparsity-preserving noise model,
this model can efficiently work with edge lists representations of graphs,
paving the way for scalability to much larger structures. During the sampling
phase, SparseDiff iteratively populates the adjacency matrix from its prior
state, ensuring prediction of the full graph while controlling memory
utilization. Experimental results show that SparseDiff simultaneously matches
state-of-the-art in generation performance on both small and large graphs,
highlighting the versatility of our method. | Machine Learning |
What field is the article from? | Title: Workflow-Guided Response Generation for Task-Oriented Dialogue
Abstract: Task-oriented dialogue (TOD) systems aim to achieve specific goals through
interactive dialogue. Such tasks usually involve following specific workflows,
i.e. executing a sequence of actions in a particular order. While prior work
has focused on supervised learning methods to condition on past actions, they
do not explicitly optimize for compliance to a desired workflow. In this paper,
we propose a novel framework based on reinforcement learning (RL) to generate
dialogue responses that are aligned with a given workflow. Our framework
consists of ComplianceScorer, a metric designed to evaluate how well a
generated response executes the specified action, combined with an RL
opimization process that utilizes an interactive sampling technique. We
evaluate our approach on two TOD datasets, Action-Based Conversations Dataset
(ABCD) (Chen et al., 2021a) and MultiWOZ 2.2 (Zang et al., 2020) on a range of
automated and human evaluation metrics. Our findings indicate that our RL-based
framework outperforms baselines and is effective at enerating responses that
both comply with the intended workflows while being expressed in a natural and
fluent manner. | Computational Linguistics |
What field is the article from? | Title: Calibration-free online test-time adaptation for electroencephalography motor imagery decoding
Abstract: Providing a promising pathway to link the human brain with external devices,
Brain-Computer Interfaces (BCIs) have seen notable advancements in decoding
capabilities, primarily driven by increasingly sophisticated techniques,
especially deep learning. However, achieving high accuracy in real-world
scenarios remains a challenge due to the distribution shift between sessions
and subjects. In this paper we will explore the concept of online test-time
adaptation (OTTA) to continuously adapt the model in an unsupervised fashion
during inference time. Our approach guarantees the preservation of privacy by
eliminating the requirement to access the source data during the adaptation
process. Additionally, OTTA achieves calibration-free operation by not
requiring any session- or subject-specific data. We will investigate the task
of electroencephalography (EEG) motor imagery decoding using a lightweight
architecture together with different OTTA techniques like alignment, adaptive
batch normalization, and entropy minimization. We examine two datasets and
three distinct data settings for a comprehensive analysis. Our adaptation
methods produce state-of-the-art results, potentially instigating a shift in
transfer learning for BCI decoding towards online adaptation. | Human-Computer Interaction |
What field is the article from? | Title: AI Alignment and Social Choice: Fundamental Limitations and Policy Implications
Abstract: Aligning AI agents to human intentions and values is a key bottleneck in
building safe and deployable AI applications. But whose values should AI agents
be aligned with? Reinforcement learning with human feedback (RLHF) has emerged
as the key framework for AI alignment. RLHF uses feedback from human
reinforcers to fine-tune outputs; all widely deployed large language models
(LLMs) use RLHF to align their outputs to human values. It is critical to
understand the limitations of RLHF and consider policy challenges arising from
these limitations. In this paper, we investigate a specific challenge in
building RLHF systems that respect democratic norms. Building on impossibility
results in social choice theory, we show that, under fairly broad assumptions,
there is no unique voting protocol to universally align AI systems using RLHF
through democratic processes. Further, we show that aligning AI agents with the
values of all individuals will always violate certain private ethical
preferences of an individual user i.e., universal AI alignment using RLHF is
impossible. We discuss policy implications for the governance of AI systems
built using RLHF: first, the need for mandating transparent voting rules to
hold model builders accountable. Second, the need for model builders to focus
on developing AI agents that are narrowly aligned to specific user groups. | Artificial Intelligence |
What field is the article from? | Title: Assessing Upper Limb Motor Function in the Immediate Post-Stroke Perioud Using Accelerometry
Abstract: Accelerometry has been extensively studied as an objective means of measuring
upper limb function in patients post-stroke. The objective of this paper is to
determine whether the accelerometry-derived measurements frequently used in
more long-term rehabilitation studies can also be used to monitor and rapidly
detect sudden changes in upper limb motor function in more recently
hospitalized stroke patients. Six binary classification models were created by
training on variable data window times of paretic upper limb accelerometer
feature data. The models were assessed on their effectiveness for
differentiating new input data into two classes: severe or moderately severe
motor function. The classification models yielded Area Under the Curve (AUC)
scores that ranged from 0.72 to 0.82 for 15-minute data windows to 0.77 to 0.94
for 120-minute data windows. These results served as a preliminary assessment
and a basis on which to further investigate the efficacy of using accelerometry
and machine learning to alert healthcare professionals to rapid changes in
motor function in the days immediately following a stroke. | Machine Learning |
What field is the article from? | Title: EtiCor: Corpus for Analyzing LLMs for Etiquettes
Abstract: Etiquettes are an essential ingredient of day-to-day interactions among
people. Moreover, etiquettes are region-specific, and etiquettes in one region
might contradict those in other regions. In this paper, we propose EtiCor, an
Etiquettes Corpus, having texts about social norms from five different regions
across the globe. The corpus provides a test bed for evaluating LLMs for
knowledge and understanding of region-specific etiquettes. Additionally, we
propose the task of Etiquette Sensitivity. We experiment with state-of-the-art
LLMs (Delphi, Falcon40B, and GPT-3.5). Initial results indicate that LLMs,
mostly fail to understand etiquettes from regions from non-Western world. | Computational Linguistics |
What field is the article from? | Title: VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding
Abstract: Recent advancements in language-model-based video understanding have been
progressing at a remarkable pace, spurred by the introduction of Large Language
Models (LLMs). However, the focus of prior research has been predominantly on
devising a projection layer that maps video features to tokens, an approach
that is both rudimentary and inefficient. In our study, we introduce a
cutting-edge framework, VaQuitA, designed to refine the synergy between video
and textual information. At the data level, instead of sampling frames
uniformly, we implement a sampling method guided by CLIP-score rankings, which
enables a more aligned selection of frames with the given question. At the
feature level, we integrate a trainable Video Perceiver alongside a
Visual-Query Transformer (abbreviated as VQ-Former), which bolsters the
interplay between the input question and the video features. We also discover
that incorporating a simple prompt, "Please be critical", into the LLM input
can substantially enhance its video comprehension capabilities. Our
experimental results indicate that VaQuitA consistently sets a new benchmark
for zero-shot video question-answering tasks and is adept at producing
high-quality, multi-turn video dialogues with users. | Computer Vision |
What field is the article from? | Title: Zero-shot Translation of Attention Patterns in VQA Models to Natural Language
Abstract: Converting a model's internals to text can yield human-understandable
insights about the model. Inspired by the recent success of training-free
approaches for image captioning, we propose ZS-A2T, a zero-shot framework that
translates the transformer attention of a given model into natural language
without requiring any training. We consider this in the context of Visual
Question Answering (VQA). ZS-A2T builds on a pre-trained large language model
(LLM), which receives a task prompt, question, and predicted answer, as inputs.
The LLM is guided to select tokens which describe the regions in the input
image that the VQA model attended to. Crucially, we determine this similarity
by exploiting the text-image matching capabilities of the underlying VQA model.
Our framework does not require any training and allows the drop-in replacement
of different guiding sources (e.g. attribution instead of attention maps), or
language models. We evaluate this novel task on textual explanation datasets
for VQA, giving state-of-the-art performances for the zero-shot setting on
GQA-REX and VQA-X. Our code is available at:
https://github.com/ExplainableML/ZS-A2T. | Computer Vision |
What field is the article from? | Title: MLLMs-Augmented Visual-Language Representation Learning
Abstract: Visual-language pre-training (VLP) has achieved remarkable success in
multi-modal tasks, largely attributed to the availability of large-scale
image-text datasets. In this work, we demonstrate that multi-modal large
language models (MLLMs) can enhance visual-language representation learning by
improving data quality. Our approach is simple, utilizing MLLMs to extend
multiple captions for each image. To prevent the bias introduced by MLLMs'
hallucinations and intrinsic caption styles, we propose "text shearing" to
maintain the same length for extended captions as that of the original
captions. In image-text retrieval, our method consistently obtains 5.6 ~ 35.0%
and 16.8 ~ 46.1% improvement on R@1 under the fine-tuning and zero-shot
settings, respectively. Notably, we obtain zero-shot results that are
comparable to fine-tuning on target datasets, which encourages more exploration
of the versatile use of MLLMs. | Computer Vision |
What field is the article from? | Title: GPT Struct Me: Probing GPT Models on Narrative Entity Extraction
Abstract: The importance of systems that can extract structured information from
textual data becomes increasingly pronounced given the ever-increasing volume
of text produced on a daily basis. Having a system that can effectively extract
such information in an interoperable manner would be an asset for several
domains, be it finance, health, or legal. Recent developments in natural
language processing led to the production of powerful language models that can,
to some degree, mimic human intelligence. Such effectiveness raises a pertinent
question: Can these models be leveraged for the extraction of structured
information? In this work, we address this question by evaluating the
capabilities of two state-of-the-art language models -- GPT-3 and GPT-3.5,
commonly known as ChatGPT -- in the extraction of narrative entities, namely
events, participants, and temporal expressions. This study is conducted on the
Text2Story Lusa dataset, a collection of 119 Portuguese news articles whose
annotation framework includes a set of entity structures along with several
tags and attribute values. We first select the best prompt template through an
ablation study over prompt components that provide varying degrees of
information on a subset of documents of the dataset. Subsequently, we use the
best templates to evaluate the effectiveness of the models on the remaining
documents. The results obtained indicate that GPT models are competitive with
out-of-the-box baseline systems, presenting an all-in-one alternative for
practitioners with limited resources. By studying the strengths and limitations
of these models in the context of information extraction, we offer insights
that can guide future improvements and avenues to explore in this field. | Computational Linguistics |
What field is the article from? | Title: Forbidden Facts: An Investigation of Competing Objectives in Llama-2
Abstract: LLMs often face competing pressures (for example helpfulness vs.
harmlessness). To understand how models resolve such conflicts, we study
Llama-2-chat models on the forbidden fact task. Specifically, we instruct
Llama-2 to truthfully complete a factual recall statement while forbidding it
from saying the correct answer. This often makes the model give incorrect
answers. We decompose Llama-2 into 1000+ components, and rank each one with
respect to how useful it is for forbidding the correct answer. We find that in
aggregate, around 35 components are enough to reliably implement the full
suppression behavior. However, these components are fairly heterogeneous and
many operate using faulty heuristics. We discover that one of these heuristics
can be exploited via a manually designed adversarial attack which we call The
California Attack. Our results highlight some roadblocks standing in the way of
being able to successfully interpret advanced ML systems. Project website
available at https://forbiddenfacts.github.io . | Machine Learning |
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