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What field is the article from? | Title: Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching
Abstract: The lightweight "local-match-global" matching introduced by SRe2L
successfully creates a distilled dataset with comprehensive information on the
full 224x224 ImageNet-1k. However, this one-sided approach is limited to a
particular backbone, layer, and statistics, which limits the improvement of the
generalization of a distilled dataset. We suggest that sufficient and various
"local-match-global" matching are more precise and effective than a single one
and has the ability to create a distilled dataset with richer information and
better generalization. We call this perspective "generalized matching" and
propose Generalized Various Backbone and Statistical Matching (G-VBSM) in this
work, which aims to create a synthetic dataset with densities, ensuring
consistency with the complete dataset across various backbones, layers, and
statistics. As experimentally demonstrated, G-VBSM is the first algorithm to
obtain strong performance across both small-scale and large-scale datasets.
Specifically, G-VBSM achieves a performance of 38.7% on CIFAR-100 with
128-width ConvNet, 47.6% on Tiny-ImageNet with ResNet18, and 31.4% on the full
224x224 ImageNet-1k with ResNet18, under images per class (IPC) 10, 50, and 10,
respectively. These results surpass all SOTA methods by margins of 3.9%, 6.5%,
and 10.1%, respectively. | Computer Vision |
What field is the article from? | Title: VRPTEST: Evaluating Visual Referring Prompting in Large Multimodal Models
Abstract: With recent advancements in Large Multimodal Models (LMMs) across various
domains, a novel prompting method called visual referring prompting has
emerged, showing significant potential in enhancing human-computer interaction
within multimodal systems. This method offers a more natural and flexible
approach to human interaction with these systems compared to traditional text
descriptions or coordinates. However, the categorization of visual referring
prompting remains undefined, and its impact on the performance of LMMs has yet
to be formally examined. In this study, we conduct the first comprehensive
analysis of LMMs using a variety of visual referring prompting strategies. We
introduce a benchmark dataset called VRPTEST, comprising 3 different visual
tasks and 2,275 images, spanning diverse combinations of prompt strategies.
Using VRPTEST, we conduct a comprehensive evaluation of eight versions of
prominent open-source and proprietary foundation models, including two early
versions of GPT-4V. We develop an automated assessment framework based on
software metamorphic testing techniques to evaluate the accuracy of LMMs
without the need for human intervention or manual labeling. We find that the
current proprietary models generally outperform the open-source ones, showing
an average accuracy improvement of 22.70%; however, there is still potential
for improvement. Moreover, our quantitative analysis shows that the choice of
prompt strategy significantly affects the accuracy of LMMs, with variations
ranging from -17.5% to +7.3%. Further case studies indicate that an appropriate
visual referring prompting strategy can improve LMMs' understanding of context
and location information, while an unsuitable one might lead to answer
rejection. We also provide insights on minimizing the negative impact of visual
referring prompting on LMMs. | Computer Vision |
What field is the article from? | Title: In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
Abstract: Large language models (LLMs) demonstrate emergent in-context learning
capabilities, where they adapt to new tasks based on example demonstrations.
However, in-context learning has seen limited effectiveness in many settings,
is difficult to quantitatively control and takes up context window space. To
overcome these limitations, we propose an alternative approach that recasts
in-context learning as in-context vectors (ICV). Using ICV has two steps. We
first use a forward pass on demonstration examples to create the in-context
vector from the latent embedding of the LLM. This vector captures essential
information about the intended task. On a new query, instead of adding
demonstrations to the prompt, we shift the latent states of the LLM using the
ICV. The ICV approach has several benefits: 1) it enables the LLM to more
effectively follow the demonstration examples; 2) it's easy to control by
adjusting the magnitude of the ICV; 3) it reduces the length of the prompt by
removing the in-context demonstrations; 4) ICV is computationally much more
efficient than fine-tuning. We demonstrate that ICV achieves better performance
compared to standard in-context learning and fine-tuning on diverse tasks
including safety, style transfer, role-playing and formatting. Moreover, we
show that we can flexibly teach LLM to simultaneously follow different types of
instructions by simple vector arithmetics on the corresponding ICVs. | Machine Learning |
What field is the article from? | Title: PINNs-Based Uncertainty Quantification for Transient Stability Analysis
Abstract: This paper addresses the challenge of transient stability in power systems
with missing parameters and uncertainty propagation in swing equations. We
introduce a novel application of Physics-Informed Neural Networks (PINNs),
specifically an Ensemble of PINNs (E-PINNs), to estimate critical parameters
like rotor angle and inertia coefficient with enhanced accuracy and reduced
computational load. E-PINNs capitalize on the underlying physical principles of
swing equations to provide a robust solution. Our approach not only facilitates
efficient parameter estimation but also quantifies uncertainties, delivering
probabilistic insights into the system behavior. The efficacy of E-PINNs is
demonstrated through the analysis of $1$-bus and $2$-bus systems, highlighting
the model's ability to handle parameter variability and data scarcity. The
study advances the application of machine learning in power system stability,
paving the way for reliable and computationally efficient transient stability
analysis. | Artificial Intelligence |
What field is the article from? | Title: MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection
Abstract: Detecting anomalies in real-world multivariate time series data is
challenging due to complex temporal dependencies and inter-variable
correlations. Recently, reconstruction-based deep models have been widely used
to solve the problem. However, these methods still suffer from an
over-generalization issue and fail to deliver consistently high performance. To
address this issue, we propose the MEMTO, a memory-guided Transformer using a
reconstruction-based approach. It is designed to incorporate a novel memory
module that can learn the degree to which each memory item should be updated in
response to the input data. To stabilize the training procedure, we use a
two-phase training paradigm which involves using K-means clustering for
initializing memory items. Additionally, we introduce a bi-dimensional
deviation-based detection criterion that calculates anomaly scores considering
both input space and latent space. We evaluate our proposed method on five
real-world datasets from diverse domains, and it achieves an average anomaly
detection F1-score of 95.74%, significantly outperforming the previous
state-of-the-art methods. We also conduct extensive experiments to empirically
validate the effectiveness of our proposed model's key components. | Machine Learning |
What field is the article from? | Title: Effectively Fine-tune to Improve Large Multimodal Models for Radiology Report Generation
Abstract: Writing radiology reports from medical images requires a high level of domain
expertise. It is time-consuming even for trained radiologists and can be
error-prone for inexperienced radiologists. It would be appealing to automate
this task by leveraging generative AI, which has shown drastic progress in
vision and language understanding. In particular, Large Language Models (LLM)
have demonstrated impressive capabilities recently and continued to set new
state-of-the-art performance on almost all natural language tasks. While many
have proposed architectures to combine vision models with LLMs for multimodal
tasks, few have explored practical fine-tuning strategies. In this work, we
proposed a simple yet effective two-stage fine-tuning protocol to align visual
features to LLM's text embedding space as soft visual prompts. Our framework
with OpenLLaMA-7B achieved state-of-the-art level performance without
domain-specific pretraining. Moreover, we provide detailed analyses of soft
visual prompts and attention mechanisms, shedding light on future research
directions. | Computer Vision |
What field is the article from? | Title: Automating the Correctness Assessment of AI-generated Code for Security Contexts
Abstract: In this paper, we propose a fully automated method, named ACCA, to evaluate
the correctness of AI-generated code for security purposes. The method uses
symbolic execution to assess whether the AI-generated code behaves as a
reference implementation. We use ACCA to assess four state-of-the-art models
trained to generate security-oriented assembly code and compare the results of
the evaluation with different baseline solutions, including output similarity
metrics, widely used in the field, and the well-known ChatGPT, the AI-powered
language model developed by OpenAI. Our experiments show that our method
outperforms the baseline solutions and assesses the correctness of the
AI-generated code similar to the human-based evaluation, which is considered
the ground truth for the assessment in the field. Moreover, ACCA has a very
strong correlation with human evaluation (Pearson's correlation coefficient
r=0.84 on average). Finally, since it is a fully automated solution that does
not require any human intervention, the proposed method performs the assessment
of every code snippet in ~0.17s on average, which is definitely lower than the
average time required by human analysts to manually inspect the code, based on
our experience. | Software Engineering |
What field is the article from? | Title: Human Conditional Reasoning in Answer Set Programming
Abstract: Given a conditional sentence P=>Q (if P then Q) and respective facts, four
different types of inferences are observed in human reasoning. Affirming the
antecedent (AA) (or modus ponens) reasons Q from P; affirming the consequent
(AC) reasons P from Q; denying the antecedent (DA) reasons -Q from -P; and
denying the consequent (DC) (or modus tollens) reasons -P from -Q. Among them,
AA and DC are logically valid, while AC and DA are logically invalid and often
called logical fallacies. Nevertheless, humans often perform AC or DA as
pragmatic inference in daily life. In this paper, we realize AC, DA and DC
inferences in answer set programming. Eight different types of completion are
introduced and their semantics are given by answer sets. We investigate formal
properties and characterize human reasoning tasks in cognitive psychology.
Those completions are also applied to commonsense reasoning in AI. | Artificial Intelligence |
What field is the article from? | Title: Which AI Technique Is Better to Classify Requirements? An Experiment with SVM, LSTM, and ChatGPT
Abstract: Context and motivation: Recently, Large Language Models (LLMs) like ChatGPT
have demonstrated remarkable proficiency in various Natural Language Processing
(NLP) tasks. Their application in Requirements Engineering (RE), especially in
requirements classification, has gained increasing interest. Question/problem:
In our research, we conducted an extensive empirical evaluation of ChatGPT
models including text-davinci-003, gpt-3.5-turbo, and gpt-4 in both zero-shot
and few-shot settings for requirements classification. The question arises as
to how these models compare to traditional classification methods, specifically
Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). Principal
ideas/results: Based on five diverse datasets, our results show that ChatGPT
consistently outperforms LSTM, and while ChatGPT is more effective than SVM in
classifying functional requirements (FR), SVM is better in classifying
non-functional requirements (NFR). Our results also show that contrary to our
expectations, the few-shot setting does not always lead to enhanced
performance; in most instances, it was found to be suboptimal. Contribution:
Our findings underscore the potential of LLMs in the RE domain, suggesting that
they could play a pivotal role in future software engineering processes,
particularly as tools to enhance requirements classification. | Artificial Intelligence |
What field is the article from? | Title: Large Language Models in Law: A Survey
Abstract: The advent of artificial intelligence (AI) has significantly impacted the
traditional judicial industry. Moreover, recently, with the development of
AI-generated content (AIGC), AI and law have found applications in various
domains, including image recognition, automatic text generation, and
interactive chat. With the rapid emergence and growing popularity of large
models, it is evident that AI will drive transformation in the traditional
judicial industry. However, the application of legal large language models
(LLMs) is still in its nascent stage. Several challenges need to be addressed.
In this paper, we aim to provide a comprehensive survey of legal LLMs. We not
only conduct an extensive survey of LLMs, but also expose their applications in
the judicial system. We first provide an overview of AI technologies in the
legal field and showcase the recent research in LLMs. Then, we discuss the
practical implementation presented by legal LLMs, such as providing legal
advice to users and assisting judges during trials. In addition, we explore the
limitations of legal LLMs, including data, algorithms, and judicial practice.
Finally, we summarize practical recommendations and propose future development
directions to address these challenges. | Computational Linguistics |
What field is the article from? | Title: MACE: A Multi-pattern Accommodated and Efficient Anomaly Detection Method in the Frequency Domain
Abstract: Anomaly detection significantly enhances the robustness of cloud systems.
While neural network-based methods have recently demonstrated strong
advantages, they encounter practical challenges in cloud environments: the
contradiction between the impracticality of maintaining a unique model for each
service and the limited ability of dealing with diverse normal patterns by a
unified model, as well as issues with handling heavy traffic in real time and
short-term anomaly detection sensitivity. Thus, we propose MACE, a
Multi-pattern Accommodated and efficient Anomaly detection method in the
frequency domain for time series anomaly detection. There are three novel
characteristics of it: (i) a pattern extraction mechanism excelling at handling
diverse normal patterns, which enables the model to identify anomalies by
examining the correlation between the data sample and its service normal
pattern, instead of solely focusing on the data sample itself; (ii) a dualistic
convolution mechanism that amplifies short-term anomalies in the time domain
and hinders the reconstruction of anomalies in the frequency domain, which
enlarges the reconstruction error disparity between anomaly and normality and
facilitates anomaly detection; (iii) leveraging the sparsity and parallelism of
frequency domain to enhance model efficiency. We theoretically and
experimentally prove that using a strategically selected subset of Fourier
bases can not only reduce computational overhead but is also profit to
distinguish anomalies, compared to using the complete spectrum. Moreover,
extensive experiments demonstrate MACE's effectiveness in handling diverse
normal patterns with a unified model and it achieves state-of-the-art
performance with high efficiency. \end{abstract} | Machine Learning |
What field is the article from? | Title: MGAS: Multi-Granularity Architecture Search for Trade-Off Between Model Effectiveness and Efficiency
Abstract: Neural architecture search (NAS) has gained significant traction in
automating the design of neural networks. To reduce the time cost,
differentiable architecture search (DAS) transforms the traditional paradigm of
discrete candidate sampling and evaluation into that of differentiable
super-net optimization and discretization. However, existing DAS methods fail
to trade off between model performance and model size. They either only conduct
coarse-grained operation-level search, which results in redundant model
parameters, or restrictively explore fine-grained filter-level and weight-level
units with pre-defined remaining ratios, suffering from excessive pruning
problem. Additionally, these methods compromise search quality to save memory
during the search process. To tackle these issues, we introduce
multi-granularity architecture search (MGAS), a unified framework which aims to
discover both effective and efficient neural networks by comprehensively yet
memory-efficiently exploring the multi-granularity search space. Specifically,
we improve the existing DAS methods in two aspects. First, we balance the model
unit numbers at different granularity levels with adaptive pruning. We learn
discretization functions specific to each granularity level to adaptively
determine the unit remaining ratio according to the evolving architecture.
Second, we reduce the memory consumption without degrading the search quality
using multi-stage search. We break down the super-net optimization and
discretization into multiple sub-net stages, and perform progressive
re-evaluation to allow for re-pruning and regrowing of previous units during
subsequent stages, compensating for potential bias. Extensive experiments on
CIFAR-10, CIFAR-100 and ImageNet demonstrate that MGAS outperforms other
state-of-the-art methods in achieving a better trade-off between model
performance and model size. | Machine Learning |
What field is the article from? | Title: Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models
Abstract: Data augmentation has become a standard component of vision pre-trained
models to capture the invariance between augmented views. In practice,
augmentation techniques that mask regions of a sample with zero/mean values or
patches from other samples are commonly employed in pre-trained models with
self-/semi-/fully-supervised contrastive losses. However, the underlying
mechanism behind the effectiveness of these augmentation techniques remains
poorly explored. To investigate the problems, we conduct an empirical study to
quantify how data augmentation affects performance. Concretely, we apply 4
types of data augmentations termed with Random Erasing, CutOut, CutMix and
MixUp to a series of self-/semi-/fully- supervised pre-trained models. We
report their performance on vision tasks such as image classification, object
detection, instance segmentation, and semantic segmentation. We then explicitly
evaluate the invariance and diversity of the feature embedding. We observe
that: 1) Masking regions of the images decreases the invariance of the learned
feature embedding while providing a more considerable diversity. 2) Manual
annotations do not change the invariance or diversity of the learned feature
embedding. 3) The MixUp approach improves the diversity significantly, with
only a marginal decrease in terms of the invariance. | Computer Vision |
What field is the article from? | Title: Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model
Abstract: In the realm of language models, the nuanced linguistic and cultural
intricacies of Traditional Chinese, as spoken in Taiwan, have been largely
overlooked. This paper introduces Taiwan LLM, a pioneering Large Language Model
that specifically caters to the Traditional Chinese language, with a focus on
the variant used in Taiwan. Leveraging a comprehensive pretraining corpus and
instruction-finetuning datasets, we have developed a model that not only
understands the complexities of Traditional Chinese but also embodies the
cultural context of Taiwan. Taiwan LLM represents the first of its kind, a
model that is not only linguistically accurate but also culturally resonant
with its user base. Our evaluations demonstrate that Taiwan LLM achieves
superior performance in understanding and generating Traditional Chinese text,
outperforming existing models that are predominantly trained on Simplified
Chinese or English. The open-source release of Taiwan LLM invites collaboration
and further innovation, ensuring that the linguistic diversity of Chinese
speakers is embraced and well-served. The model, datasets, and further
resources are made publicly available to foster ongoing research and
development in this field. | Computational Linguistics |
What field is the article from? | Title: Rare Event Probability Learning by Normalizing Flows
Abstract: A rare event is defined by a low probability of occurrence. Accurate
estimation of such small probabilities is of utmost importance across diverse
domains. Conventional Monte Carlo methods are inefficient, demanding an
exorbitant number of samples to achieve reliable estimates. Inspired by the
exact sampling capabilities of normalizing flows, we revisit this challenge and
propose normalizing flow assisted importance sampling, termed NOFIS. NOFIS
first learns a sequence of proposal distributions associated with predefined
nested subset events by minimizing KL divergence losses. Next, it estimates the
rare event probability by utilizing importance sampling in conjunction with the
last proposal. The efficacy of our NOFIS method is substantiated through
comprehensive qualitative visualizations, affirming the optimality of the
learned proposal distribution, as well as a series of quantitative experiments
encompassing $10$ distinct test cases, which highlight NOFIS's superiority over
baseline approaches. | Machine Learning |
What field is the article from? | Title: In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment
Abstract: Driving is a complex daily activity indicating age and disease related
cognitive declines. Therefore, deficits in driving performance compared with
ones without mild cognitive impairment (MCI) can reflect changes in cognitive
functioning. There is increasing evidence that unobtrusive monitoring of older
adults driving performance in a daily-life setting may allow us to detect
subtle early changes in cognition. The objectives of this paper include
designing low-cost in-vehicle sensing hardware capable of obtaining
high-precision positioning and telematics data, identifying important
indicators for early changes in cognition, and detecting early-warning signs of
cognitive impairment in a truly normal, day-to-day driving condition with
machine learning approaches. Our statistical analysis comparing drivers with
MCI to those without reveals that those with MCI exhibit smoother and safer
driving patterns. This suggests that drivers with MCI are cognizant of their
condition and tend to avoid erratic driving behaviors. Furthermore, our Random
Forest models identified the number of night trips, number of trips, and
education as the most influential factors in our data evaluation. | Human-Computer Interaction |
What field is the article from? | Title: Graph Pre-training and Prompt Learning for Recommendation
Abstract: GNN-based recommenders have excelled in modeling intricate user-item
interactions through multi-hop message passing. However, existing methods often
overlook the dynamic nature of evolving user-item interactions, which impedes
the adaption to changing user preferences and distribution shifts in newly
arriving data. Thus, their scalability and performances in real-world dynamic
environments are limited. In this study, we propose GraphPL, a framework that
incorporates parameter-efficient and dynamic graph pre-training with prompt
learning. This novel combination empowers GNNs to effectively capture both
long-term user preferences and short-term behavior dynamics, enabling the
delivery of accurate and timely recommendations. Our GraphPL framework
addresses the challenge of evolving user preferences by seamlessly integrating
a temporal prompt mechanism and a graph-structural prompt learning mechanism
into the pre-trained GNN model. The temporal prompt mechanism encodes time
information on user-item interaction, allowing the model to naturally capture
temporal context, while the graph-structural prompt learning mechanism enables
the transfer of pre-trained knowledge to adapt to behavior dynamics without the
need for continuous incremental training. We further bring in a dynamic
evaluation setting for recommendation to mimic real-world dynamic scenarios and
bridge the offline-online gap to a better level. Our extensive experiments
including a large-scale industrial deployment showcases the lightweight plug-in
scalability of our GraphPL when integrated with various state-of-the-art
recommenders, emphasizing the advantages of GraphPL in terms of effectiveness,
robustness and efficiency. | Information Retrieval |
What field is the article from? | Title: ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations
Abstract: Electronic theses and dissertations (ETDs) have been proposed, advocated, and
generated for more than 25 years. Although ETDs are hosted by commercial or
institutional digital library repositories, they are still an understudied type
of scholarly big data, partially because they are usually longer than
conference proceedings and journals. Segmenting ETDs will allow researchers to
study sectional content. Readers can navigate to particular pages of interest,
discover, and explore the content buried in these long documents. Most existing
frameworks on document page classification are designed for classifying general
documents and perform poorly on ETDs. In this paper, we propose ETDPC. Its
backbone is a two-stream multimodal model with a cross-attention network to
classify ETD pages into 13 categories. To overcome the challenge of imbalanced
labeled samples, we augmented data for minority categories and employed a
hierarchical classifier. ETDPC outperforms the state-of-the-art models in all
categories, achieving an F1 of 0.84 -- 0.96 for 9 out of 13 categories. We also
demonstrated its data efficiency. The code and data can be found on GitHub
(https://github.com/lamps-lab/ETDMiner/tree/master/etd_segmentation). | Computer Vision |
What field is the article from? | Title: Addressing Sample Inefficiency in Multi-View Representation Learning
Abstract: Non-contrastive self-supervised learning (NC-SSL) methods like BarlowTwins
and VICReg have shown great promise for label-free representation learning in
computer vision. Despite the apparent simplicity of these techniques,
researchers must rely on several empirical heuristics to achieve competitive
performance, most notably using high-dimensional projector heads and two
augmentations of the same image. In this work, we provide theoretical insights
on the implicit bias of the BarlowTwins and VICReg loss that can explain these
heuristics and guide the development of more principled recommendations. Our
first insight is that the orthogonality of the features is more critical than
projector dimensionality for learning good representations. Based on this, we
empirically demonstrate that low-dimensional projector heads are sufficient
with appropriate regularization, contrary to the existing heuristic. Our second
theoretical insight suggests that using multiple data augmentations better
represents the desiderata of the SSL objective. Based on this, we demonstrate
that leveraging more augmentations per sample improves representation quality
and trainability. In particular, it improves optimization convergence, leading
to better features emerging earlier in the training. Remarkably, we demonstrate
that we can reduce the pretraining dataset size by up to 4x while maintaining
accuracy and improving convergence simply by using more data augmentations.
Combining these insights, we present practical pretraining recommendations that
improve wall-clock time by 2x and improve performance on CIFAR-10/STL-10
datasets using a ResNet-50 backbone. Thus, this work provides a theoretical
insight into NC-SSL and produces practical recommendations for enhancing its
sample and compute efficiency. | Machine Learning |
What field is the article from? | Title: MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
Abstract: Large language models (LLMs) can potentially democratize access to medical
knowledge. While many efforts have been made to harness and improve LLMs'
medical knowledge and reasoning capacities, the resulting models are either
closed-source (e.g., PaLM, GPT-4) or limited in scale (<= 13B parameters),
which restricts their abilities. In this work, we improve access to large-scale
medical LLMs by releasing MEDITRON: a suite of open-source LLMs with 7B and 70B
parameters adapted to the medical domain. MEDITRON builds on Llama-2 (through
our adaptation of Nvidia's Megatron-LM distributed trainer), and extends
pretraining on a comprehensively curated medical corpus, including selected
PubMed articles, abstracts, and internationally-recognized medical guidelines.
Evaluations using four major medical benchmarks show significant performance
gains over several state-of-the-art baselines before and after task-specific
finetuning. Overall, MEDITRON achieves a 6% absolute performance gain over the
best public baseline in its parameter class and 3% over the strongest baseline
we finetuned from Llama-2. Compared to closed-source LLMs, MEDITRON-70B
outperforms GPT-3.5 and Med-PaLM and is within 5% of GPT-4 and 10% of
Med-PaLM-2. We release our code for curating the medical pretraining corpus and
the MEDITRON model weights to drive open-source development of more capable
medical LLMs. | Computational Linguistics |
What field is the article from? | Title: Joint Learning of Local and Global Features for Aspect-based Sentiment Classification
Abstract: Aspect-based sentiment classification (ASC) aims to judge the sentiment
polarity conveyed by the given aspect term in a sentence. The sentiment
polarity is not only determined by the local context but also related to the
words far away from the given aspect term. Most recent efforts related to the
attention-based models can not sufficiently distinguish which words they should
pay more attention to in some cases. Meanwhile, graph-based models are coming
into ASC to encode syntactic dependency tree information. But these models do
not fully leverage syntactic dependency trees as they neglect to incorporate
dependency relation tag information into representation learning effectively.
In this paper, we address these problems by effectively modeling the local and
global features. Firstly, we design a local encoder containing: a Gaussian mask
layer and a covariance self-attention layer. The Gaussian mask layer tends to
adjust the receptive field around aspect terms adaptively to deemphasize the
effects of unrelated words and pay more attention to local information. The
covariance self-attention layer can distinguish the attention weights of
different words more obviously. Furthermore, we propose a dual-level graph
attention network as a global encoder by fully employing dependency tag
information to capture long-distance information effectively. Our model
achieves state-of-the-art performance on both SemEval 2014 and Twitter
datasets. | Computational Linguistics |
What field is the article from? | Title: MixTEA: Semi-supervised Entity Alignment with Mixture Teaching
Abstract: Semi-supervised entity alignment (EA) is a practical and challenging task
because of the lack of adequate labeled mappings as training data. Most works
address this problem by generating pseudo mappings for unlabeled entities.
However, they either suffer from the erroneous (noisy) pseudo mappings or
largely ignore the uncertainty of pseudo mappings. In this paper, we propose a
novel semi-supervised EA method, termed as MixTEA, which guides the model
learning with an end-to-end mixture teaching of manually labeled mappings and
probabilistic pseudo mappings. We firstly train a student model using few
labeled mappings as standard. More importantly, in pseudo mapping learning, we
propose a bi-directional voting (BDV) strategy that fuses the alignment
decisions in different directions to estimate the uncertainty via the joint
matching confidence score. Meanwhile, we also design a matching diversity-based
rectification (MDR) module to adjust the pseudo mapping learning, thus reducing
the negative influence of noisy mappings. Extensive results on benchmark
datasets as well as further analyses demonstrate the superiority and the
effectiveness of our proposed method. | Machine Learning |
What field is the article from? | Title: Enhancing Actuarial Non-Life Pricing Models via Transformers
Abstract: Currently, there is a lot of research in the field of neural networks for
non-life insurance pricing. The usual goal is to improve the predictive power
via neural networks while building upon the generalized linear model, which is
the current industry standard. Our paper contributes to this current journey
via novel methods to enhance actuarial non-life models with transformer models
for tabular data. We build here upon the foundation laid out by the combined
actuarial neural network as well as the localGLMnet and enhance those models
via the feature tokenizer transformer. The manuscript demonstrates the
performance of the proposed methods on a real-world claim frequency dataset and
compares them with several benchmark models such as generalized linear models,
feed-forward neural networks, combined actuarial neural networks, LocalGLMnet,
and pure feature tokenizer transformer. The paper shows that the new methods
can achieve better results than the benchmark models while preserving certain
generalized linear model advantages. The paper also discusses the practical
implications and challenges of applying transformer models in actuarial
settings. | Machine Learning |
What field is the article from? | Title: Resource Constrained Semantic Segmentation for Waste Sorting
Abstract: This work addresses the need for efficient waste sorting strategies in
Materials Recovery Facilities to minimize the environmental impact of rising
waste. We propose resource-constrained semantic segmentation models for
segmenting recyclable waste in industrial settings. Our goal is to develop
models that fit within a 10MB memory constraint, suitable for edge applications
with limited processing capacity. We perform the experiments on three networks:
ICNet, BiSeNet (Xception39 backbone), and ENet. Given the aforementioned
limitation, we implement quantization and pruning techniques on the broader
nets, achieving positive results while marginally impacting the Mean IoU
metric. Furthermore, we propose a combination of Focal and Lov\'asz loss that
addresses the implicit class imbalance resulting in better performance compared
with the Cross-entropy loss function. | Computer Vision |
What field is the article from? | Title: How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations
Abstract: There is an increasing consensus about the effectiveness of user-centred
approaches in the explainable artificial intelligence (XAI) field. Indeed, the
number and complexity of personalised and user-centred approaches to XAI have
rapidly grown in recent years. Often, these works have a two-fold objective:
(1) proposing novel XAI techniques able to consider the users and (2) assessing
the \textit{goodness} of such techniques with respect to others. From these new
works, it emerged that user-centred approaches to XAI positively affect the
interaction between users and systems. However, so far, the goodness of XAI
systems has been measured through indirect measures, such as performance. In
this paper, we propose an assessment task to objectively and quantitatively
measure the goodness of XAI systems in terms of their \textit{information
power}, which we intended as the amount of information the system provides to
the users during the interaction. Moreover, we plan to use our task to
objectively compare two XAI techniques in a human-robot decision-making task to
understand deeper whether user-centred approaches are more informative than
classical ones. | Artificial Intelligence |
What field is the article from? | Title: Isometric Motion Manifold Primitives
Abstract: The Motion Manifold Primitive (MMP) produces, for a given task, a continuous
manifold of trajectories each of which can successfully complete the task. It
consists of the decoder function that parametrizes the manifold and the
probability density in the latent coordinate space. In this paper, we first
show that the MMP performance can significantly degrade due to the geometric
distortion in the latent space -- by distortion, we mean that similar motions
are not located nearby in the latent space. We then propose {\it Isometric
Motion Manifold Primitives (IMMP)} whose latent coordinate space preserves the
geometry of the manifold. For this purpose, we formulate and use a Riemannian
metric for the motion space (i.e., parametric curve space), which we call a
{\it CurveGeom Riemannian metric}. Experiments with planar obstacle-avoiding
motions and pushing manipulation tasks show that IMMP significantly outperforms
existing MMP methods. Code is available at
https://github.com/Gabe-YHLee/IMMP-public. | Artificial Intelligence |
What field is the article from? | Title: CRISPR: Eliminating Bias Neurons from an Instruction-following Language Model
Abstract: Large language models (LLMs) executing tasks through instruction-based
prompts often face challenges stemming from distribution differences between
user instructions and training instructions. This leads to distractions and
biases, especially when dealing with inconsistent dynamic labels. In this
paper, we introduces a novel bias mitigation method, CRISPR, designed to
alleviate instruction-label biases in LLMs. CRISPR utilizes attribution methods
to identify bias neurons influencing biased outputs and employs pruning to
eliminate the bias neurons. Experimental results demonstrate the method's
effectiveness in mitigating biases in instruction-based prompting, enhancing
language model performance on social bias benchmarks without compromising
pre-existing knowledge. CRISPR proves highly practical, model-agnostic,
offering flexibility in adapting to evolving social biases. | Artificial Intelligence |
What field is the article from? | Title: Visually Grounded Language Learning: a review of language games, datasets, tasks, and models
Abstract: In recent years, several machine learning models have been proposed. They are
trained with a language modelling objective on large-scale text-only data. With
such pretraining, they can achieve impressive results on many Natural Language
Understanding and Generation tasks. However, many facets of meaning cannot be
learned by ``listening to the radio" only. In the literature, many
Vision+Language (V+L) tasks have been defined with the aim of creating models
that can ground symbols in the visual modality. In this work, we provide a
systematic literature review of several tasks and models proposed in the V+L
field. We rely on Wittgenstein's idea of `language games' to categorise such
tasks into 3 different families: 1) discriminative games, 2) generative games,
and 3) interactive games. Our analysis of the literature provides evidence that
future work should be focusing on interactive games where communication in
Natural Language is important to resolve ambiguities about object referents and
action plans and that physical embodiment is essential to understand the
semantics of situations and events. Overall, these represent key requirements
for developing grounded meanings in neural models. | Computational Linguistics |
What field is the article from? | Title: TimeDRL: Disentangled Representation Learning for Multivariate Time-Series
Abstract: Multivariate time-series data in numerous real-world applications (e.g.,
healthcare and industry) are informative but challenging due to the lack of
labels and high dimensionality. Recent studies in self-supervised learning have
shown their potential in learning rich representations without relying on
labels, yet they fall short in learning disentangled embeddings and addressing
issues of inductive bias (e.g., transformation-invariance). To tackle these
challenges, we propose TimeDRL, a generic multivariate time-series
representation learning framework with disentangled dual-level embeddings.
TimeDRL is characterized by three novel features: (i) disentangled derivation
of timestamp-level and instance-level embeddings from patched time-series data
using a [CLS] token strategy; (ii) utilization of timestamp-predictive and
instance-contrastive tasks for disentangled representation learning, with the
former optimizing timestamp-level embeddings with predictive loss, and the
latter optimizing instance-level embeddings with contrastive loss; and (iii)
avoidance of augmentation methods to eliminate inductive biases, such as
transformation-invariance from cropping and masking. Comprehensive experiments
on 6 time-series forecasting datasets and 5 time-series classification datasets
have shown that TimeDRL consistently surpasses existing representation learning
approaches, achieving an average improvement of forecasting by 57.98% in MSE
and classification by 1.25% in accuracy. Furthermore, extensive ablation
studies confirmed the relative contribution of each component in TimeDRL's
architecture, and semi-supervised learning evaluations demonstrated its
effectiveness in real-world scenarios, even with limited labeled data. | Machine Learning |
What field is the article from? | Title: Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans?
Abstract: Vision-Language Models (VLMs) are trained on vast amounts of data captured by
humans emulating our understanding of the world. However, known as visual
illusions, human's perception of reality isn't always faithful to the physical
world. This raises a key question: do VLMs have the similar kind of illusions
as humans do, or do they faithfully learn to represent reality? To investigate
this question, we build a dataset containing five types of visual illusions and
formulate four tasks to examine visual illusions in state-of-the-art VLMs. Our
findings have shown that although the overall alignment is low, larger models
are closer to human perception and more susceptible to visual illusions. Our
dataset and initial findings will promote a better understanding of visual
illusions in humans and machines and provide a stepping stone for future
computational models that can better align humans and machines in perceiving
and communicating about the shared visual world. The code and data are
available at https://github.com/vl-illusion/dataset. | Artificial Intelligence |
What field is the article from? | Title: Injecting linguistic knowledge into BERT for Dialogue State Tracking
Abstract: Dialogue State Tracking (DST) models often employ intricate neural network
architectures, necessitating substantial training data, and their inference
processes lack transparency. This paper proposes a method that extracts
linguistic knowledge via an unsupervised framework and subsequently utilizes
this knowledge to augment BERT's performance and interpretability in DST tasks.
The knowledge extraction procedure is computationally economical and does not
necessitate annotations or additional training data. The injection of the
extracted knowledge necessitates the addition of only simple neural modules. We
employ the Convex Polytopic Model (CPM) as a feature extraction tool for DST
tasks and illustrate that the acquired features correlate with the syntactic
and semantic patterns in the dialogues. This correlation facilitates a
comprehensive understanding of the linguistic features influencing the DST
model's decision-making process. We benchmark this framework on various DST
tasks and observe a notable improvement in accuracy. | Computational Linguistics |
What field is the article from? | Title: Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
Abstract: Off-Policy Evaluation (OPE) aims to assess the effectiveness of
counterfactual policies using only offline logged data and is often used to
identify the top-k promising policies for deployment in online A/B tests.
Existing evaluation metrics for OPE estimators primarily focus on the
"accuracy" of OPE or that of downstream policy selection, neglecting
risk-return tradeoff in the subsequent online policy deployment. To address
this issue, we draw inspiration from portfolio evaluation in finance and
develop a new metric, called SharpeRatio@k, which measures the risk-return
tradeoff of policy portfolios formed by an OPE estimator under varying online
evaluation budgets (k). We validate our metric in two example scenarios,
demonstrating its ability to effectively distinguish between low-risk and
high-risk estimators and to accurately identify the most efficient estimator.
This efficient estimator is characterized by its capability to form the most
advantageous policy portfolios, maximizing returns while minimizing risks
during online deployment, a nuance that existing metrics typically overlook. To
facilitate a quick, accurate, and consistent evaluation of OPE via
SharpeRatio@k, we have also integrated this metric into an open-source
software, SCOPE-RL. Employing SharpeRatio@k and SCOPE-RL, we conduct
comprehensive benchmarking experiments on various estimators and RL tasks,
focusing on their risk-return tradeoff. These experiments offer several
interesting directions and suggestions for future OPE research. | Machine Learning |
What field is the article from? | Title: LLMs Accelerate Annotation for Medical Information Extraction
Abstract: The unstructured nature of clinical notes within electronic health records
often conceals vital patient-related information, making it challenging to
access or interpret. To uncover this hidden information, specialized Natural
Language Processing (NLP) models are required. However, training these models
necessitates large amounts of labeled data, a process that is both
time-consuming and costly when relying solely on human experts for annotation.
In this paper, we propose an approach that combines Large Language Models
(LLMs) with human expertise to create an efficient method for generating ground
truth labels for medical text annotation. By utilizing LLMs in conjunction with
human annotators, we significantly reduce the human annotation burden, enabling
the rapid creation of labeled datasets. We rigorously evaluate our method on a
medical information extraction task, demonstrating that our approach not only
substantially cuts down on human intervention but also maintains high accuracy.
The results highlight the potential of using LLMs to improve the utilization of
unstructured clinical data, allowing for the swift deployment of tailored NLP
solutions in healthcare. | Computational Linguistics |
What field is the article from? | Title: Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning
Abstract: Assurance Cases (ACs) are an established approach in safety engineering to
argue quality claims in a structured way. In the context of quality assurance
for Machine Learning (ML)-based software components, ACs are also being
discussed and appear promising. Tools for operationalizing ACs do exist, yet
mainly focus on supporting safety engineers on the system level. However,
assuring the quality of an ML component within the system is commonly the
responsibility of data scientists, who are usually less familiar with these
tools. To address this gap, we propose a framework to support the
operationalization of ACs for ML components based on technologies that data
scientists use on a daily basis: Python and Jupyter Notebook. Our aim is to
make the process of creating ML-related evidence in ACs more effective. Results
from the application of the framework, documented through notebooks, can be
integrated into existing AC tools. We illustrate the application of the
framework on an example excerpt concerned with the quality of the test data. | Software Engineering |
What field is the article from? | Title: Creative Agents: Empowering Agents with Imagination for Creative Tasks
Abstract: We study building embodied agents for open-ended creative tasks. While
existing methods build instruction-following agents that can perform diverse
open-ended tasks, none of them demonstrates creativity -- the ability to give
novel and diverse task solutions implicit in the language instructions. This
limitation comes from their inability to convert abstract language instructions
into concrete task goals in the environment and perform long-horizon planning
for such complicated goals. Given the observation that humans perform creative
tasks with the help of imagination, we propose a class of solutions for
creative agents, where the controller is enhanced with an imaginator that
generates detailed imaginations of task outcomes conditioned on language
instructions. We introduce several approaches to implementing the components of
creative agents. We implement the imaginator with either a large language model
for textual imagination or a diffusion model for visual imagination. The
controller can either be a behavior-cloning policy learned from data or a
pre-trained foundation model generating executable codes in the environment. We
benchmark creative tasks with the challenging open-world game Minecraft, where
the agents are asked to create diverse buildings given free-form language
instructions. In addition, we propose novel evaluation metrics for open-ended
creative tasks utilizing GPT-4V, which holds many advantages over existing
metrics. We perform a detailed experimental analysis of creative agents,
showing that creative agents are the first AI agents accomplishing diverse
building creation in the survival mode of Minecraft. Our benchmark and models
are open-source for future research on creative agents
(https://github.com/PKU-RL/Creative-Agents). | Artificial Intelligence |
What field is the article from? | Title: CHAIN: Exploring Global-Local Spatio-Temporal Information for Improved Self-Supervised Video Hashing
Abstract: Compressing videos into binary codes can improve retrieval speed and reduce
storage overhead. However, learning accurate hash codes for video retrieval can
be challenging due to high local redundancy and complex global dependencies
between video frames, especially in the absence of labels. Existing
self-supervised video hashing methods have been effective in designing
expressive temporal encoders, but have not fully utilized the temporal dynamics
and spatial appearance of videos due to less challenging and unreliable
learning tasks. To address these challenges, we begin by utilizing the
contrastive learning task to capture global spatio-temporal information of
videos for hashing. With the aid of our designed augmentation strategies, which
focus on spatial and temporal variations to create positive pairs, the learning
framework can generate hash codes that are invariant to motion, scale, and
viewpoint. Furthermore, we incorporate two collaborative learning tasks, i.e.,
frame order verification and scene change regularization, to capture local
spatio-temporal details within video frames, thereby enhancing the perception
of temporal structure and the modeling of spatio-temporal relationships. Our
proposed Contrastive Hashing with Global-Local Spatio-temporal Information
(CHAIN) outperforms state-of-the-art self-supervised video hashing methods on
four video benchmark datasets. Our codes will be released. | Computer Vision |
What field is the article from? | Title: Remembering to Be Fair: On Non-Markovian Fairness in Sequential DecisionMaking (Preliminary Report)
Abstract: Fair decision making has largely been studied with respect to a single
decision. In this paper we investigate the notion of fairness in the context of
sequential decision making where multiple stakeholders can be affected by the
outcomes of decisions, and where decision making may be informed by additional
constraints and criteria beyond the requirement of fairness. In this setting,
we observe that fairness often depends on the history of the sequential
decision-making process and not just on the current state. To advance our
understanding of this class of fairness problems, we define the notion of
non-Markovian fairness in the context of sequential decision making. We
identify properties of non-Markovian fairness, including notions of long-term,
anytime, periodic, and bounded fairness. We further explore the interplay
between non-Markovian fairness and memory, and how this can support
construction of fair policies in sequential decision-making settings. | Artificial Intelligence |
What field is the article from? | Title: Correlation and Unintended Biases on Univariate and Multivariate Decision Trees
Abstract: Decision Trees are accessible, interpretable, and well-performing
classification models. A plethora of variants with increasing expressiveness
has been proposed in the last forty years. We contrast the two families of
univariate DTs, whose split functions partition data through axis-parallel
hyperplanes, and multivariate DTs, whose splits instead partition data through
oblique hyperplanes. The latter include the former, hence multivariate DTs are
in principle more powerful. Surprisingly enough, however, univariate DTs
consistently show comparable performances in the literature. We analyze the
reasons behind this, both with synthetic and real-world benchmark datasets. Our
research questions test whether the pre-processing phase of removing
correlation among features in datasets has an impact on the relative
performances of univariate vs multivariate DTs. We find that existing benchmark
datasets are likely biased towards favoring univariate DTs. | Machine Learning |
What field is the article from? | Title: LLM aided semi-supervision for Extractive Dialog Summarization
Abstract: Generating high-quality summaries for chat dialogs often requires large
labeled datasets. We propose a method to efficiently use unlabeled data for
extractive summarization of customer-agent dialogs. In our method, we frame
summarization as a question-answering problem and use state-of-the-art large
language models (LLMs) to generate pseudo-labels for a dialog. We then use
these pseudo-labels to fine-tune a chat summarization model, effectively
transferring knowledge from the large LLM into a smaller specialized model. We
demonstrate our method on the \tweetsumm dataset, and show that using 10% of
the original labelled data set we can achieve 65.9/57.0/61.0 ROUGE-1/-2/-L,
whereas the current state-of-the-art trained on the entire training data set
obtains 65.16/55.81/64.37 ROUGE-1/-2/-L. In other words, in the worst case
(i.e., ROUGE-L) we still effectively retain 94.7% of the performance while
using only 10% of the data. | Computational Linguistics |
What field is the article from? | Title: Open-Set Object Recognition Using Mechanical Properties During Interaction
Abstract: while most of the tactile robots are operated in close-set conditions, it is
challenging for them to operate in open-set conditions where test objects are
beyond the robots' knowledge. We proposed an open-set recognition framework
using mechanical properties to recongise known objects and incrementally label
novel objects. The main contribution is a clustering algorithm that exploits
knowledge of known objects to estimate cluster centre and sizes, unlike a
typical algorithm that randomly selects them. The framework is validated with
the mechanical properties estimated from a real object during interaction. The
results show that the framework could recognise objects better than alternative
methods contributed by the novelty detector. Importantly, our clustering
algorithm yields better clustering performance than other methods. Furthermore,
the hyperparameters studies show that cluster size is important to clustering
results and needed to be tuned properly. | Robotics |
What field is the article from? | Title: Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing
Abstract: GUI testing checks if a software system behaves as expected when users
interact with its graphical interface, e.g., testing specific functionality or
validating relevant use case scenarios. Currently, deciding what to test at
this high level is a manual task since automated GUI testing tools target lower
level adequacy metrics such as structural code coverage or activity coverage.
We propose DroidAgent, an autonomous GUI testing agent for Android, for
semantic, intent-driven automation of GUI testing. It is based on Large
Language Models and support mechanisms such as long- and short-term memory.
Given an Android app, DroidAgent sets relevant task goals and subsequently
tries to achieve them by interacting with the app. Our empirical evaluation of
DroidAgent using 15 apps from the Themis benchmark shows that it can set up and
perform realistic tasks, with a higher level of autonomy. For example, when
testing a messaging app, DroidAgent created a second account and added a first
account as a friend, testing a realistic use case, without human intervention.
On average, DroidAgent achieved 61% activity coverage, compared to 51% for
current state-of-the-art GUI testing techniques. Further, manual analysis shows
that 317 out of the 374 autonomously created tasks are realistic and relevant
to app functionalities, and also that DroidAgent interacts deeply with the apps
and covers more features. | Software Engineering |
What field is the article from? | Title: Tailoring Mixup to Data using Kernel Warping functions
Abstract: Data augmentation is an essential building block for learning efficient deep
learning models. Among all augmentation techniques proposed so far, linear
interpolation of training data points, also called mixup, has found to be
effective for a large panel of applications. While the majority of works have
focused on selecting the right points to mix, or applying complex non-linear
interpolation, we are interested in mixing similar points more frequently and
strongly than less similar ones. To this end, we propose to dynamically change
the underlying distribution of interpolation coefficients through warping
functions, depending on the similarity between data points to combine. We
define an efficient and flexible framework to do so without losing in
diversity. We provide extensive experiments for classification and regression
tasks, showing that our proposed method improves both performance and
calibration of models. Code available in
https://github.com/ENSTA-U2IS/torch-uncertainty | Machine Learning |
What field is the article from? | Title: LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers
Abstract: Logical reasoning, i.e., deductively inferring the truth value of a
conclusion from a set of premises, is an important task for artificial
intelligence with wide potential impacts on science, mathematics, and society.
While many prompting-based strategies have been proposed to enable Large
Language Models (LLMs) to do such reasoning more effectively, they still appear
unsatisfactory, often failing in subtle and unpredictable ways. In this work,
we investigate the validity of instead reformulating such tasks as modular
neurosymbolic programming, which we call LINC: Logical Inference via
Neurosymbolic Computation. In LINC, the LLM acts as a semantic parser,
translating premises and conclusions from natural language to expressions in
first-order logic. These expressions are then offloaded to an external theorem
prover, which symbolically performs deductive inference. Leveraging this
approach, we observe significant performance gains on FOLIO and a balanced
subset of ProofWriter for three different models in nearly all experimental
conditions we evaluate. On ProofWriter, augmenting the comparatively small
open-source StarCoder+ (15.5B parameters) with LINC even outperforms GPT-3.5
and GPT-4 with Chain-of-Thought (CoT) prompting by an absolute 38% and 10%,
respectively. When used with GPT-4, LINC scores 26% higher than CoT on
ProofWriter while performing comparatively on FOLIO. Further analysis reveals
that although both methods on average succeed roughly equally often on this
dataset, they exhibit distinct and complementary failure modes. We thus provide
promising evidence for how logical reasoning over natural language can be
tackled through jointly leveraging LLMs alongside symbolic provers. All
corresponding code is publicly available at https://github.com/benlipkin/linc | Computational Linguistics |
What field is the article from? | Title: Unsupervised Temporal Action Localization via Self-paced Incremental Learning
Abstract: Recently, temporal action localization (TAL) has garnered significant
interest in information retrieval community. However, existing
supervised/weakly supervised methods are heavily dependent on extensive labeled
temporal boundaries and action categories, which is labor-intensive and
time-consuming. Although some unsupervised methods have utilized the
``iteratively clustering and localization'' paradigm for TAL, they still suffer
from two pivotal impediments: 1) unsatisfactory video clustering confidence,
and 2) unreliable video pseudolabels for model training. To address these
limitations, we present a novel self-paced incremental learning model to
enhance clustering and localization training simultaneously, thereby
facilitating more effective unsupervised TAL. Concretely, we improve the
clustering confidence through exploring the contextual feature-robust visual
information. Thereafter, we design two (constant- and variable- speed)
incremental instance learning strategies for easy-to-hard model training, thus
ensuring the reliability of these video pseudolabels and further improving
overall localization performance. Extensive experiments on two public datasets
have substantiated the superiority of our model over several state-of-the-art
competitors. | Computer Vision |
What field is the article from? | Title: Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play
Abstract: Infants' ability to recognize and categorize objects develops gradually. The
second year of life is marked by both the emergence of more semantic visual
representations and a better understanding of word meaning. This suggests that
language input may play an important role in shaping visual representations.
However, even in suitable contexts for word learning like dyadic play sessions,
caregivers utterances are sparse and ambiguous, often referring to objects that
are different from the one to which the child attends. Here, we systematically
investigate to what extent caregivers' utterances can nevertheless enhance
visual representations. For this we propose a computational model of visual
representation learning during dyadic play. We introduce a synthetic dataset of
ego-centric images perceived by a toddler-agent that moves and rotates toy
objects in different parts of its home environment while hearing caregivers'
utterances, modeled as captions. We propose to model toddlers' learning as
simultaneously aligning representations for 1) close-in-time images and 2)
co-occurring images and utterances. We show that utterances with statistics
matching those of real caregivers give rise to representations supporting
improved category recognition. Our analysis reveals that a small
decrease/increase in object-relevant naming frequencies can drastically impact
the learned representations. This affects the attention on object names within
an utterance, which is required for efficient visuo-linguistic alignment.
Overall, our results support the hypothesis that caregivers' naming utterances
can improve toddlers' visual representations. | Computer Vision |
What field is the article from? | Title: Distributed AI in Zero-touch Provisioning for Edge Networks: Challenges and Research Directions
Abstract: Zero-touch network is anticipated to inaugurate the generation of intelligent
and highly flexible resource provisioning strategies where multiple service
providers collaboratively offer computation and storage resources. This
transformation presents substantial challenges to network administration and
service providers regarding sustainability and scalability. This article
combines Distributed Artificial Intelligence (DAI) with Zero-touch Provisioning
(ZTP) for edge networks. This combination helps to manage network devices
seamlessly and intelligently by minimizing human intervention. In addition,
several advantages are also highlighted that come with incorporating
Distributed AI into ZTP in the context of edge networks. Further, we draw
potential research directions to foster novel studies in this field and
overcome the current limitations. | Artificial Intelligence |
What field is the article from? | Title: FacadeNet: Conditional Facade Synthesis via Selective Editing
Abstract: We introduce FacadeNet, a deep learning approach for synthesizing building
facade images from diverse viewpoints. Our method employs a conditional GAN,
taking a single view of a facade along with the desired viewpoint information
and generates an image of the facade from the distinct viewpoint. To precisely
modify view-dependent elements like windows and doors while preserving the
structure of view-independent components such as walls, we introduce a
selective editing module. This module leverages image embeddings extracted from
a pre-trained vision transformer. Our experiments demonstrated state-of-the-art
performance on building facade generation, surpassing alternative methods. | Computer Vision |
What field is the article from? | Title: Data Scarcity in Recommendation Systems: A Survey
Abstract: The prevalence of online content has led to the widespread adoption of
recommendation systems (RSs), which serve diverse purposes such as news,
advertisements, and e-commerce recommendations. Despite their significance,
data scarcity issues have significantly impaired the effectiveness of existing
RS models and hindered their progress. To address this challenge, the concept
of knowledge transfer, particularly from external sources like pre-trained
language models, emerges as a potential solution to alleviate data scarcity and
enhance RS development. However, the practice of knowledge transfer in RSs is
intricate. Transferring knowledge between domains introduces data disparities,
and the application of knowledge transfer in complex RS scenarios can yield
negative consequences if not carefully designed. Therefore, this article
contributes to this discourse by addressing the implications of data scarcity
on RSs and introducing various strategies, such as data augmentation,
self-supervised learning, transfer learning, broad learning, and knowledge
graph utilization, to mitigate this challenge. Furthermore, it delves into the
challenges and future direction within the RS domain, offering insights that
are poised to facilitate the development and implementation of robust RSs,
particularly when confronted with data scarcity. We aim to provide valuable
guidance and inspiration for researchers and practitioners, ultimately driving
advancements in the field of RS. | Information Retrieval |
What field is the article from? | Title: ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation
Abstract: Despite remarkable advances that large language models have achieved in
chatbots, maintaining a non-toxic user-AI interactive environment has become
increasingly critical nowadays. However, previous efforts in toxicity detection
have been mostly based on benchmarks derived from social media content, leaving
the unique challenges inherent to real-world user-AI interactions
insufficiently explored. In this work, we introduce ToxicChat, a novel
benchmark based on real user queries from an open-source chatbot. This
benchmark contains the rich, nuanced phenomena that can be tricky for current
toxicity detection models to identify, revealing a significant domain
difference compared to social media content. Our systematic evaluation of
models trained on existing toxicity datasets has shown their shortcomings when
applied to this unique domain of ToxicChat. Our work illuminates the
potentially overlooked challenges of toxicity detection in real-world user-AI
conversations. In the future, ToxicChat can be a valuable resource to drive
further advancements toward building a safe and healthy environment for user-AI
interactions. | Computational Linguistics |
What field is the article from? | Title: Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG
Abstract: Walking-assistive devices require adaptive control methods to ensure smooth
transitions between various modes of locomotion. For this purpose, detecting
human locomotion modes (e.g., level walking or stair ascent) in advance is
crucial for improving the intelligence and transparency of such robotic
systems. This study proposes Deep-STF, a unified end-to-end deep learning model
designed for integrated feature extraction in spatial, temporal, and frequency
dimensions from surface electromyography (sEMG) signals. Our model enables
accurate and robust continuous prediction of nine locomotion modes and 15
transitions at varying prediction time intervals, ranging from 100 to 500 ms.
In addition, we introduced the concept of 'stable prediction time' as a
distinct metric to quantify prediction efficiency. This term refers to the
duration during which consistent and accurate predictions of mode transitions
are made, measured from the time of the fifth correct prediction to the
occurrence of the critical event leading to the task transition. This
distinction between stable prediction time and prediction time is vital as it
underscores our focus on the precision and reliability of mode transition
predictions. Experimental results showcased Deep-STP's cutting-edge prediction
performance across diverse locomotion modes and transitions, relying solely on
sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other
machine learning techniques, achieving an outstanding average prediction
accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy
only marginally decreased to 93.00%. The averaged stable prediction times for
detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the
100-500 ms time advances. | Robotics |
What field is the article from? | Title: Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent
Abstract: Conversational agents leveraging AI, particularly deep learning, are emerging
in both academic research and real-world applications. However, these
applications still face challenges, including disrespecting knowledge and
facts, not personalizing to user preferences, and enormous demand for
computational resources during training and inference. Recent research efforts
have been focused on addressing these challenges from various aspects,
including supplementing various types of auxiliary information to the
conversational agents. However, existing methods are still not able to
effectively and efficiently exploit relevant information from these auxiliary
supplements to further unleash the power of the conversational agents and the
language models they use. In this paper, we present a novel method, PK-NCLI,
that is able to accurately and efficiently identify relevant auxiliary
information to improve the quality of conversational responses by learning the
relevance among persona, chat history, and knowledge background through
low-level normalized contextual latent interaction. Our experimental results
indicate that PK-NCLI outperforms the state-of-the-art method, PK-FoCus, by
47.80%/30.61%/24.14% in terms of perplexity, knowledge grounding, and training
efficiency, respectively, and maintained the same level of persona grounding
performance. We also provide a detailed analysis of how different factors,
including language model choices and trade-offs on training weights, would
affect the performance of PK-NCLI. | Computational Linguistics |
What field is the article from? | Title: Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences
Abstract: Distributed learning has emerged as a leading paradigm for training large
machine learning models. However, in real-world scenarios, participants may be
unreliable or malicious, posing a significant challenge to the integrity and
accuracy of the trained models. Byzantine fault tolerance mechanisms have been
proposed to address these issues, but they often assume full participation from
all clients, which is not always practical due to the unavailability of some
clients or communication constraints. In our work, we propose the first
distributed method with client sampling and provable tolerance to Byzantine
workers. The key idea behind the developed method is the use of gradient
clipping to control stochastic gradient differences in recursive variance
reduction. This allows us to bound the potential harm caused by Byzantine
workers, even during iterations when all sampled clients are Byzantine.
Furthermore, we incorporate communication compression into the method to
enhance communication efficiency. Under quite general assumptions, we prove
convergence rates for the proposed method that match the existing
state-of-the-art (SOTA) theoretical results. | Machine Learning |
What field is the article from? | Title: AlignBench: Benchmarking Chinese Alignment of Large Language Models
Abstract: Alignment has become a critical step for instruction-tuned Large Language
Models (LLMs) to become helpful assistants. However, effective evaluation of
alignment for emerging Chinese LLMs is still significantly lacking, calling for
real-scenario grounded, open-ended, challenging and automatic evaluations
tailored for alignment. To fill in this gap, we introduce AlignBench, a
comprehensive multi-dimensional benchmark for evaluating LLMs' alignment in
Chinese. Equipped with a human-in-the-loop data curation pipeline, our
benchmark employs a rule-calibrated multi-dimensional LLM-as-Judge with
Chain-of-Thought to generate explanations and final ratings as evaluations,
ensuring high reliability and interpretability. Furthermore, we report
AlignBench evaluated by CritiqueLLM, a dedicated Chinese evaluator LLM that
recovers 95% of GPT-4's evaluation ability. We will provide public APIs for
evaluating AlignBench with CritiqueLLM to facilitate the evaluation of LLMs'
Chinese alignment. All evaluation codes, data, and LLM generations are
available at \url{https://github.com/THUDM/AlignBench}. | Computational Linguistics |
What field is the article from? | Title: Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor
Abstract: Federated learning encounters a critical challenge of data heterogeneity,
adversely affecting the performance and convergence of the federated model.
Various approaches have been proposed to address this issue, yet their
effectiveness is still limited. Recent studies have revealed that the federated
model suffers severe forgetting in local training, leading to global forgetting
and performance degradation. Although the analysis provides valuable insights,
a comprehensive understanding of the vulnerable classes and their impact
factors is yet to be established. In this paper, we aim to bridge this gap by
systematically analyzing the forgetting degree of each class during local
training across different communication rounds. Our observations are: (1) Both
missing and non-dominant classes suffer similar severe forgetting during local
training, while dominant classes show improvement in performance. (2) When
dynamically reducing the sample size of a dominant class, catastrophic
forgetting occurs abruptly when the proportion of its samples is below a
certain threshold, indicating that the local model struggles to leverage a few
samples of a specific class effectively to prevent forgetting. Motivated by
these findings, we propose a novel and straightforward algorithm called
Federated Knowledge Anchor (FedKA). Assuming that all clients have a single
shared sample for each class, the knowledge anchor is constructed before each
local training stage by extracting shared samples for missing classes and
randomly selecting one sample per class for non-dominant classes. The knowledge
anchor is then utilized to correct the gradient of each mini-batch towards the
direction of preserving the knowledge of the missing and non-dominant classes.
Extensive experimental results demonstrate that our proposed FedKA achieves
fast and stable convergence, significantly improving accuracy on popular
benchmarks. | Machine Learning |
What field is the article from? | Title: Beyond Average Return in Markov Decision Processes
Abstract: What are the functionals of the reward that can be computed and optimized
exactly in Markov Decision Processes? In the finite-horizon, undiscounted
setting, Dynamic Programming (DP) can only handle these operations efficiently
for certain classes of statistics. We summarize the characterization of these
classes for policy evaluation, and give a new answer for the planning problem.
Interestingly, we prove that only generalized means can be optimized exactly,
even in the more general framework of Distributional Reinforcement Learning
(DistRL).DistRL permits, however, to evaluate other functionals approximately.
We provide error bounds on the resulting estimators, and discuss the potential
of this approach as well as its limitations.These results contribute to
advancing the theory of Markov Decision Processes by examining overall
characteristics of the return, and particularly risk-conscious strategies. | Artificial Intelligence |
What field is the article from? | Title: Unknown Sample Discovery for Source Free Open Set Domain Adaptation
Abstract: Open Set Domain Adaptation (OSDA) aims to adapt a model trained on a source
domain to a target domain that undergoes distribution shift and contains
samples from novel classes outside the source domain. Source-free OSDA
(SF-OSDA) techniques eliminate the need to access source domain samples, but
current SF-OSDA methods utilize only the known classes in the target domain for
adaptation, and require access to the entire target domain even during
inference after adaptation, to make the distinction between known and unknown
samples. In this paper, we introduce Unknown Sample Discovery (USD) as an
SF-OSDA method that utilizes a temporally ensembled teacher model to conduct
known-unknown target sample separation and adapts the student model to the
target domain over all classes using co-training and temporal consistency
between the teacher and the student. USD promotes Jensen-Shannon distance (JSD)
as an effective measure for known-unknown sample separation. Our
teacher-student framework significantly reduces error accumulation resulting
from imperfect known-unknown sample separation, while curriculum guidance helps
to reliably learn the distinction between target known and target unknown
subspaces. USD appends the target model with an unknown class node, thus
readily classifying a target sample into any of the known or unknown classes in
subsequent post-adaptation inference stages. Empirical results show that USD is
superior to existing SF-OSDA methods and is competitive with current OSDA
models that utilize both source and target domains during adaptation. | Computer Vision |
What field is the article from? | Title: EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
Abstract: We present EE-LLM, a framework for large-scale training and inference of
early-exit large language models (LLMs). While recent works have shown
preliminary evidence for the efficacy of early exiting in accelerating LLM
inference, EE-LLM makes a foundational step towards scaling up early-exit LLMs
by supporting their training and inference with massive 3D parallelism. Built
upon Megatron-LM, EE-LLM implements a variety of algorithmic innovations and
performance optimizations tailored to early exiting, including a lightweight
method that facilitates backpropagation for the early-exit training objective
with pipeline parallelism, techniques of leveraging idle resources in the
original pipeline schedule for computation related to early-exit layers, and
two approaches of early-exit inference that are compatible with KV caching for
autoregressive generation. Our analytical and empirical study shows that EE-LLM
achieves great training efficiency with negligible computational overhead
compared to standard LLM training, as well as outstanding inference speedup
without compromising output quality. To facilitate further research and
adoption, we release EE-LLM at https://github.com/pan-x-c/EE-LLM. | Machine Learning |
What field is the article from? | Title: Casual Social Media Use among the Youth: Effects on Online and Offline Political Participation
Abstract: Background: Previous studies suggest that social media use among the youth is
correlated with online and offline political participation. There is also a
mixed and inconclusive debate on whether more online political participation in
the youth increases their offline political participation. Methods: This study
uses three models of OLS, two-way fixed effects, and an instrumental variable
approach to make causal inferences about social media use, online, and offline
political participation of the youth. Findings: The analyses provide evidence
of a large effect of casual social media use on online political participation,
and no effect or negligible effect on offline political participation and
voting behavior. The results from fixed effects and instrumental variable
models provide strong evidence of elasticity between online and offline
political participation in young individuals. On average, a one percent
increase in online political participation increases the offline political
activity index by 0.12 percent. | Computers and Society |
What field is the article from? | Title: Two-Step Reinforcement Learning for Multistage Strategy Card Game
Abstract: In the realm of artificial intelligence and card games, this study introduces
a two-step reinforcement learning (RL) strategy tailored for "The Lord of the
Rings: The Card Game (LOTRCG)," a complex multistage strategy card game. This
research diverges from conventional RL methods by adopting a phased learning
approach, beginning with a foundational learning stage in a simplified version
of the game and subsequently progressing to the complete, intricate game
environment. This methodology notably enhances the AI agent's adaptability and
performance in the face of LOTRCG's unpredictable and challenging nature. The
paper also explores a multi-agent system, where distinct RL agents are employed
for various decision-making aspects of the game. This approach has demonstrated
a remarkable improvement in game outcomes, with the RL agents achieving a
winrate of 78.5% across a set of 10,000 random games. | Artificial Intelligence |
What field is the article from? | Title: Online Advertisements with LLMs: Opportunities and Challenges
Abstract: This paper explores the potential for leveraging Large Language Models (LLM)
in the realm of online advertising systems. We delve into essential
requirements including privacy, latency, reliability, users and advertisers'
satisfaction, which such a system must fulfill. We further introduce a general
framework for LLM advertisement, consisting of modification, bidding,
prediction, and auction modules. Different design considerations for each
module is presented, with an in-depth examination of their practicality and the
technical challenges inherent to their implementation. | Computers and Society |
What field is the article from? | Title: Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning
Abstract: Personal sensing, leveraging data passively and near-continuously collected
with wearables from patients in their ecological environment, is a promising
paradigm to monitor mood disorders (MDs), a major determinant of worldwide
disease burden. However, collecting and annotating wearable data is very
resource-intensive. Studies of this kind can thus typically afford to recruit
only a couple dozens of patients. This constitutes one of the major obstacles
to applying modern supervised machine learning techniques to MDs detection. In
this paper, we overcome this data bottleneck and advance the detection of MDs
acute episode vs stable state from wearables data on the back of recent
advances in self-supervised learning (SSL). This leverages unlabelled data to
learn representations during pre-training, subsequently exploited for a
supervised task. First, we collected open-access datasets recording with an
Empatica E4 spanning different, unrelated to MD monitoring, personal sensing
tasks -- from emotion recognition in Super Mario players to stress detection in
undergraduates -- and devised a pre-processing pipeline performing on-/off-body
detection, sleep-wake detection, segmentation, and (optionally) feature
extraction. With 161 E4-recorded subjects, we introduce E4SelfLearning, the
largest to date open access collection, and its pre-processing pipeline.
Second, we show that SSL confidently outperforms fully-supervised pipelines
using either our novel E4-tailored Transformer architecture (E4mer) or
classical baseline XGBoost: 81.23% against 75.35% (E4mer) and 72.02% (XGBoost)
correctly classified recording segments from 64 (half acute, half stable)
patients. Lastly, we illustrate that SSL performance is strongly associated
with the specific surrogate task employed for pre-training as well as with
unlabelled data availability. | Machine Learning |
What field is the article from? | Title: Sample-Efficient Learning to Solve a Real-World Labyrinth Game Using Data-Augmented Model-Based Reinforcement Learning
Abstract: Motivated by the challenge of achieving rapid learning in physical
environments, this paper presents the development and training of a robotic
system designed to navigate and solve a labyrinth game using model-based
reinforcement learning techniques. The method involves extracting
low-dimensional observations from camera images, along with a cropped and
rectified image patch centered on the current position within the labyrinth,
providing valuable information about the labyrinth layout. The learning of a
control policy is performed purely on the physical system using model-based
reinforcement learning, where the progress along the labyrinth's path serves as
a reward signal. Additionally, we exploit the system's inherent symmetries to
augment the training data. Consequently, our approach learns to successfully
solve a popular real-world labyrinth game in record time, with only 5 hours of
real-world training data. | Robotics |
What field is the article from? | Title: Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning
Abstract: Action advising endeavors to leverage supplementary guidance from expert
teachers to alleviate the issue of sampling inefficiency in Deep Reinforcement
Learning (DRL). Previous agent-specific action advising methods are hindered by
imperfections in the agent itself, while agent-agnostic approaches exhibit
limited adaptability to the learning agent. In this study, we propose a novel
framework called Agent-Aware trAining yet Agent-Agnostic Action Advising (A7)
to strike a balance between the two. The underlying concept of A7 revolves
around utilizing the similarity of state features as an indicator for
soliciting advice. However, unlike prior methodologies, the measurement of
state feature similarity is performed by neither the error-prone learning agent
nor the agent-agnostic advisor. Instead, we employ a proxy model to extract
state features that are both discriminative (adaptive to the agent) and
generally applicable (robust to agent noise). Furthermore, we utilize behavior
cloning to train a model for reusing advice and introduce an intrinsic reward
for the advised samples to incentivize the utilization of expert guidance.
Experiments are conducted on the GridWorld, LunarLander, and six prominent
scenarios from Atari games. The results demonstrate that A7 significantly
accelerates the learning process and surpasses existing methods (both
agent-specific and agent-agnostic) by a substantial margin. Our code will be
made publicly available. | Artificial Intelligence |
What field is the article from? | Title: Defense Against Adversarial Attacks using Convolutional Auto-Encoders
Abstract: Deep learning models, while achieving state-of-the-art performance on many
tasks, are susceptible to adversarial attacks that exploit inherent
vulnerabilities in their architectures. Adversarial attacks manipulate the
input data with imperceptible perturbations, causing the model to misclassify
the data or produce erroneous outputs. This work is based on enhancing the
robustness of targeted classifier models against adversarial attacks. To
achieve this, an convolutional autoencoder-based approach is employed that
effectively counters adversarial perturbations introduced to the input images.
By generating images closely resembling the input images, the proposed
methodology aims to restore the model's accuracy. | Computer Vision |
What field is the article from? | Title: Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation
Abstract: Diffusion Transformers have recently shown remarkable effectiveness in
generating high-quality 3D point clouds. However, training voxel-based
diffusion models for high-resolution 3D voxels remains prohibitively expensive
due to the cubic complexity of attention operators, which arises from the
additional dimension of voxels. Motivated by the inherent redundancy of 3D
compared to 2D, we propose FastDiT-3D, a novel masked diffusion transformer
tailored for efficient 3D point cloud generation, which greatly reduces
training costs. Specifically, we draw inspiration from masked autoencoders to
dynamically operate the denoising process on masked voxelized point clouds. We
also propose a novel voxel-aware masking strategy to adaptively aggregate
background/foreground information from voxelized point clouds. Our method
achieves state-of-the-art performance with an extreme masking ratio of nearly
99%. Moreover, to improve multi-category 3D generation, we introduce
Mixture-of-Expert (MoE) in 3D diffusion model. Each category can learn a
distinct diffusion path with different experts, relieving gradient conflict.
Experimental results on the ShapeNet dataset demonstrate that our method
achieves state-of-the-art high-fidelity and diverse 3D point cloud generation
performance. Our FastDiT-3D improves 1-Nearest Neighbor Accuracy and Coverage
metrics when generating 128-resolution voxel point clouds, using only 6.5% of
the original training cost. | Computer Vision |
What field is the article from? | Title: ChatGPT-Powered Hierarchical Comparisons for Image Classification
Abstract: The zero-shot open-vocabulary challenge in image classification is tackled by
pretrained vision-language models like CLIP, which benefit from incorporating
class-specific knowledge from large language models (LLMs) like ChatGPT.
However, biases in CLIP lead to similar descriptions for distinct but related
classes, prompting our novel image classification framework via hierarchical
comparisons: using LLMs to recursively group classes into hierarchies and
classifying images by comparing image-text embeddings at each hierarchy level,
resulting in an intuitive, effective, and explainable approach. | Computer Vision |
What field is the article from? | Title: Neural Implicit Field Editing Considering Object-environment Interaction
Abstract: The 3D scene editing method based on neural implicit field has gained wide
attention. It has achieved excellent results in 3D editing tasks. However,
existing methods often blend the interaction between objects and scene
environment. The change of scene appearance like shadows is failed to be
displayed in the rendering view. In this paper, we propose an Object and Scene
environment Interaction aware (OSI-aware) system, which is a novel two-stream
neural rendering system considering object and scene environment interaction.
To obtain illuminating conditions from the mixture soup, the system
successfully separates the interaction between objects and scene environment by
intrinsic decomposition method. To study the corresponding changes to the scene
appearance from object-level editing tasks, we introduce a depth map guided
scene inpainting method and shadow rendering method by point matching strategy.
Extensive experiments demonstrate that our novel pipeline produce reasonable
appearance changes in scene editing tasks. It also achieve competitive
performance for the rendering quality in novel-view synthesis tasks. | Computer Vision |
What field is the article from? | Title: Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
Abstract: Large language models (LLMs) have dramatically enhanced the field of language
intelligence, as demonstrably evidenced by their formidable empirical
performance across a spectrum of complex reasoning tasks. Additionally,
theoretical proofs have illuminated their emergent reasoning capabilities,
providing a compelling showcase of their advanced cognitive abilities in
linguistic contexts. Critical to their remarkable efficacy in handling complex
reasoning tasks, LLMs leverage the intriguing chain-of-thought (CoT) reasoning
techniques, obliging them to formulate intermediate steps en route to deriving
an answer. The CoT reasoning approach has not only exhibited proficiency in
amplifying reasoning performance but also in enhancing interpretability,
controllability, and flexibility. In light of these merits, recent research
endeavors have extended CoT reasoning methodologies to nurture the development
of autonomous language agents, which adeptly adhere to language instructions
and execute actions within varied environments. This survey paper orchestrates
a thorough discourse, penetrating vital research dimensions, encompassing: (i)
the foundational mechanics of CoT techniques, with a focus on elucidating the
circumstances and justification behind its efficacy; (ii) the paradigm shift in
CoT; and (iii) the burgeoning of language agents fortified by CoT approaches.
Prospective research avenues envelop explorations into generalization,
efficiency, customization, scaling, and safety. This paper caters to a wide
audience, including beginners seeking comprehensive knowledge of CoT reasoning
and language agents, as well as experienced researchers interested in
foundational mechanics and engaging in cutting-edge discussions on these
topics. A repository for the related papers is available at
https://github.com/Zoeyyao27/CoT-Igniting-Agent. | Computational Linguistics |
What field is the article from? | Title: Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards
Abstract: Exploration poses a fundamental challenge in Reinforcement Learning (RL) with
sparse rewards, limiting an agent's ability to learn optimal decision-making
due to a lack of informative feedback signals. Self-Imitation Learning
(self-IL) has emerged as a promising approach for exploration, leveraging a
replay buffer to store and reproduce successful behaviors. However, traditional
self-IL methods, which rely on high-return transitions and assume singleton
environments, face challenges in generalization, especially in
procedurally-generated (PCG) environments. Therefore, new self-IL methods have
been proposed to rank which experiences to persist, but they replay transitions
uniformly regardless of their significance, and do not address the diversity of
the stored demonstrations. In this work, we propose tailored self-IL sampling
strategies by prioritizing transitions in different ways and extending
prioritization techniques to PCG environments. We also address diversity loss
through modifications to counteract the impact of generalization requirements
and bias introduced by prioritization techniques. Our experimental analysis,
conducted over three PCG sparse reward environments, including MiniGrid and
ProcGen, highlights the benefits of our proposed modifications, achieving a new
state-of-the-art performance in the MiniGrid-MultiRoom-N12-S10 environment. | Machine Learning |
What field is the article from? | Title: Grasp Force Optimization as a Bilinear Matrix Inequality Problem: A Deep Learning Approach
Abstract: Grasp force synthesis is a non-convex optimization problem involving
constraints that are bilinear. Traditional approaches to this problem involve
general-purpose gradient-based nonlinear optimization and semi-definite
programming. With a view towards dealing with postural synergies and non-smooth
but convex positive semidefinite constraints, we look beyond gradient-based
optimization. The focus of this paper is to undertake a grasp analysis of
biomimetic grasping in multi-fingered robotic hands as a bilinear matrix
inequality (BMI) problem. Our analysis is to solve it using a deep learning
approach to make the algorithm efficiently generate force closure grasps with
optimal grasp quality on untrained/unseen objects. | Robotics |
What field is the article from? | Title: Utilizing Explainability Techniques for Reinforcement Learning Model Assurance
Abstract: Explainable Reinforcement Learning (XRL) can provide transparency into the
decision-making process of a Deep Reinforcement Learning (DRL) model and
increase user trust and adoption in real-world use cases. By utilizing XRL
techniques, researchers can identify potential vulnerabilities within a trained
DRL model prior to deployment, therefore limiting the potential for mission
failure or mistakes by the system. This paper introduces the ARLIN (Assured RL
Model Interrogation) Toolkit, an open-source Python library that identifies
potential vulnerabilities and critical points within trained DRL models through
detailed, human-interpretable explainability outputs. To illustrate ARLIN's
effectiveness, we provide explainability visualizations and vulnerability
analysis for a publicly available DRL model. The open-source code repository is
available for download at https://github.com/mitre/arlin. | Machine Learning |
What field is the article from? | Title: Assessing the Interpretability of Programmatic Policies with Large Language Models
Abstract: Although the synthesis of programs encoding policies often carries the
promise of interpretability, systematic evaluations to assess the
interpretability of these policies were never performed, likely because of the
complexity of such an evaluation. In this paper, we introduce a novel metric
that uses large-language models (LLM) to assess the interpretability of
programmatic policies. For our metric, an LLM is given both a program and a
description of its associated programming language. The LLM then formulates a
natural language explanation of the program. This explanation is subsequently
fed into a second LLM, which tries to reconstruct the program from the natural
language explanation. Our metric measures the behavioral similarity between the
reconstructed program and the original. We validate our approach using
obfuscated programs that are used to solve classic programming problems. We
also assess our metric with programmatic policies synthesized for playing a
real-time strategy game, comparing the interpretability scores of programmatic
policies synthesized by an existing system to lightly obfuscated versions of
the same programs. Our LLM-based interpretability score consistently ranks less
interpretable programs lower and more interpretable ones higher. These findings
suggest that our metric could serve as a reliable and inexpensive tool for
evaluating the interpretability of programmatic policies. | Artificial Intelligence |
What field is the article from? | Title: Dissecting In-Context Learning of Translations in GPTs
Abstract: Most of the recent work in leveraging Large Language Models (LLMs) such as
GPT-3 for Machine Translation (MT) has focused on selecting the few-shot
samples for prompting. In this work, we try to better understand the role of
demonstration attributes for the in-context learning of translations through
perturbations of high-quality, in-domain demonstrations. We find that
asymmetric perturbation of the source-target mappings yield vastly different
results. We show that the perturbation of the source side has surprisingly
little impact, while target perturbation can drastically reduce translation
quality, suggesting that it is the output text distribution that provides the
most important learning signal during in-context learning of translations. We
propose a method named Zero-Shot-Context to add this signal automatically in
Zero-Shot prompting. We demonstrate that it improves upon the zero-shot
translation performance of GPT-3, even making it competitive with few-shot
prompted translations. | Computational Linguistics |
What field is the article from? | Title: Generation of Explanations for Logic Reasoning
Abstract: This thesis delves into a fortiori arguments in deductive reasoning,
underscoring their relevance in various domains such as law, philosophy, and
artificial intelligence. The research is centred on employing GPT-3.5-turbo to
automate the analysis of these arguments, with a focus on understanding
intricate reasoning processes, generating clear and coherent explanations, and
creating novel arguments. The methodology encompasses a series of tasks
including detailed reasoning, interpretation, and the augmentation of a
fortiori arguments. It involves meticulously identifying these arguments in
diverse contexts, differentiating comparative elements, and categorizing them
based on their logical structure.
Extensive experiments reveals the challenges encountered by GPT-3.5-turbo in
accurately detecting and classifying a fortiori arguments. Nevertheless, the
model demonstrates a performance that rivals specialized models, particularly
in extracting key components and interpreting underlying properties. The
integration of external information into the model's processing significantly
elevates the quality of the generated explanations. Additionally, the model
exhibits a noteworthy capability in augmenting arguments, thus contributing to
the enrichment of the data set.
Despite facing certain limitations, this thesis makes significant
contributions to the fields of artificial intelligence and logical reasoning.
It introduces novel methodologies, establishes a rigorous evaluation framework,
and provides deep insights that set the stage for future advancements in
automated logical reasoning. The findings and methodologies presented herein
not only underscore the potential of AI in complex reasoning tasks but also
highlight areas for future research and development. | Artificial Intelligence |
What field is the article from? | Title: Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model
Abstract: The Multi-Modal Large Language Model (MLLM) refers to an extension of the
Large Language Model (LLM) equipped with the capability to receive and infer
multi-modal data. Spatial awareness stands as one of the crucial abilities of
MLLM, encompassing diverse skills related to understanding spatial
relationships among objects and between objects and the scene area. Industries
such as autonomous driving, smart healthcare, robotics, virtual, and augmented
reality heavily demand MLLM's spatial awareness capabilities. However, there
exists a noticeable gap between the current spatial awareness capabilities of
MLLM and the requirements set by human needs. To address this issue, this paper
proposes using more precise spatial position information between objects to
guide MLLM in providing more accurate responses to user-related inquiries.
Specifically, for a particular multi-modal task, we utilize algorithms for
acquiring geometric spatial information and scene graphs to obtain relevant
geometric spatial information and scene details of objects involved in the
query. Subsequently, based on this information, we direct MLLM to address
spatial awareness-related queries posed by the user. Extensive experiments were
conducted in benchmarks such as MME, MM-Vet, and other multi-modal large
language models. The experimental results thoroughly confirm the efficacy of
the proposed method in enhancing the spatial awareness tasks and associated
tasks of MLLM. | Artificial Intelligence |
What field is the article from? | Title: Soulstyler: Using Large Language Model to Guide Image Style Transfer for Target Object
Abstract: Image style transfer occupies an important place in both computer graphics
and computer vision. However, most current methods require reference to
stylized images and cannot individually stylize specific objects. To overcome
this limitation, we propose the "Soulstyler" framework, which allows users to
guide the stylization of specific objects in an image through simple textual
descriptions. We introduce a large language model to parse the text and
identify stylization goals and specific styles. Combined with a CLIP-based
semantic visual embedding encoder, the model understands and matches text and
image content. We also introduce a novel localized text-image block matching
loss that ensures that style transfer is performed only on specified target
objects, while non-target regions remain in their original style. Experimental
results demonstrate that our model is able to accurately perform style transfer
on target objects according to textual descriptions without affecting the style
of background regions. Our code will be available at
https://github.com/yisuanwang/Soulstyler. | Computer Vision |
What field is the article from? | Title: Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut Triggers
Abstract: Recent applications of LLMs in Machine Reading Comprehension (MRC) systems
have shown impressive results, but the use of shortcuts, mechanisms triggered
by features spuriously correlated to the true label, has emerged as a potential
threat to their reliability. We analyze the problem from two angles: LLMs as
editors, guided to edit text to mislead LLMs; and LLMs as readers, who answer
questions based on the edited text. We introduce a framework that guides an
editor to add potential shortcuts-triggers to samples. Using GPT4 as the
editor, we find it can successfully edit trigger shortcut in samples that fool
LLMs. Analysing LLMs as readers, we observe that even capable LLMs can be
deceived using shortcut knowledge. Strikingly, we discover that GPT4 can be
deceived by its own edits (15% drop in F1). Our findings highlight inherent
vulnerabilities of LLMs to shortcut manipulations. We publish ShortcutQA, a
curated dataset generated by our framework for future research. | Computational Linguistics |
What field is the article from? | Title: Scalable Motion Style Transfer with Constrained Diffusion Generation
Abstract: Current training of motion style transfer systems relies on consistency
losses across style domains to preserve contents, hindering its scalable
application to a large number of domains and private data. Recent image
transfer works show the potential of independent training on each domain by
leveraging implicit bridging between diffusion models, with the content
preservation, however, limited to simple data patterns. We address this by
imposing biased sampling in backward diffusion while maintaining the domain
independence in the training stage. We construct the bias from the source
domain keyframes and apply them as the gradient of content constraints,
yielding a framework with keyframe manifold constraint gradients (KMCGs). Our
validation demonstrates the success of training separate models to transfer
between as many as ten dance motion styles. Comprehensive experiments find a
significant improvement in preserving motion contents in comparison to baseline
and ablative diffusion-based style transfer models. In addition, we perform a
human study for a subjective assessment of the quality of generated dance
motions. The results validate the competitiveness of KMCGs. | Computer Vision |
What field is the article from? | Title: MacGyver: Are Large Language Models Creative Problem Solvers?
Abstract: We explore the creative problem-solving capabilities of modern large language
models (LLMs) in a constrained setting. The setting requires circumventing a
cognitive bias known in psychology as ''functional fixedness'' to use familiar
objects in innovative or unconventional ways. To this end, we create MacGyver,
an automatically generated dataset consisting of 1,600 real-world problems that
deliberately trigger functional fixedness and require thinking
'out-of-the-box'. We then present our collection of problems to both LLMs and
humans to compare and contrast their problem-solving abilities. We show that
MacGyver is challenging for both groups, but in unique and complementary ways.
For example, humans typically excel in solving problems that they are familiar
with but may struggle with tasks requiring domain-specific knowledge, leading
to a higher variance. On the other hand, LLMs, being exposed to a variety of
highly specialized knowledge, attempt broader problems but are prone to
overconfidence and propose actions that are physically infeasible or
inefficient. We also provide a detailed error analysis of LLMs, and demonstrate
the potential of enhancing their problem-solving ability with novel prompting
techniques such as iterative step-wise reflection and divergent-convergent
thinking. This work provides insight into the creative problem-solving
capabilities of humans and AI and illustrates how psychological paradigms can
be extended into large-scale tasks for comparing humans and machines. | Computational Linguistics |
What field is the article from? | Title: Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models
Abstract: Large language models (LLMs) with billions of parameters and pretrained on
massive amounts of data are now capable of near or better than state-of-the-art
performance in a variety of downstream natural language processing tasks.
Neural machine translation (NMT) is one such task that LLMs have been applied
to with great success. However, little research has focused on applying LLMs to
the more difficult subset of NMT called simultaneous translation (SimulMT),
where translation begins before the entire source context is available to the
model. In this paper, we address key challenges facing LLMs fine-tuned for
SimulMT, validate classical SimulMT concepts and practices in the context of
LLMs, explore adapting LLMs that are fine-tuned for NMT to the task of SimulMT,
and introduce Simul-LLM, the first open-source fine-tuning and evaluation
pipeline development framework for LLMs focused on SimulMT. | Computational Linguistics |
What field is the article from? | Title: AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising
Abstract: Large language models (LLMs) have opened up enormous opportunities while
simultaneously posing ethical dilemmas. One of the major concerns is their
ability to create text that closely mimics human writing, which can lead to
potential misuse, such as academic misconduct, disinformation, and fraud. To
address this problem, we present AuthentiGPT, an efficient classifier that
distinguishes between machine-generated and human-written texts. Under the
assumption that human-written text resides outside the distribution of
machine-generated text, AuthentiGPT leverages a black-box LLM to denoise input
text with artificially added noise, and then semantically compares the denoised
text with the original to determine if the content is machine-generated. With
only one trainable parameter, AuthentiGPT eliminates the need for a large
training dataset, watermarking the LLM's output, or computing the
log-likelihood. Importantly, the detection capability of AuthentiGPT can be
easily adapted to any generative language model. With a 0.918 AUROC score on a
domain-specific dataset, AuthentiGPT demonstrates its effectiveness over other
commercial algorithms, highlighting its potential for detecting
machine-generated text in academic settings. | Computational Linguistics |
What field is the article from? | Title: Auto-ICL: In-Context Learning without Human Supervision
Abstract: In the era of Large Language Models (LLMs), human-computer interaction has
evolved towards natural language, offering unprecedented flexibility. Despite
this, LLMs are heavily reliant on well-structured prompts to function
efficiently within the realm of In-Context Learning. Vanilla In-Context
Learning relies on human-provided contexts, such as labeled examples, explicit
instructions, or other guiding mechanisms that shape the model's outputs. To
address this challenge, our study presents a universal framework named
Automatic In-Context Learning. Upon receiving a user's request, we ask the
model to independently generate examples, including labels, instructions, or
reasoning pathways. The model then leverages this self-produced context to
tackle the given problem. Our approach is universally adaptable and can be
implemented in any setting where vanilla In-Context Learning is applicable. We
demonstrate that our method yields strong performance across a range of tasks,
standing up well when compared to existing methods. | Machine Learning |
What field is the article from? | Title: Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
Abstract: We introduce Support Decomposition Variational Inference (SDVI), a new
variational inference (VI) approach for probabilistic programs with stochastic
support. Existing approaches to this problem rely on designing a single global
variational guide on a variable-by-variable basis, while maintaining the
stochastic control flow of the original program. SDVI instead breaks the
program down into sub-programs with static support, before automatically
building separate sub-guides for each. This decomposition significantly aids in
the construction of suitable variational families, enabling, in turn,
substantial improvements in inference performance. | Machine Learning |
What field is the article from? | Title: Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs
Abstract: The latest advancements in large language models (LLMs) have revolutionized
the field of natural language processing (NLP). Inspired by the success of LLMs
in NLP tasks, some recent work has begun investigating the potential of
applying LLMs in graph learning tasks. However, most of the existing work
focuses on utilizing LLMs as powerful node feature augmenters, leaving
employing LLMs to enhance graph topological structures an understudied problem.
In this work, we explore how to leverage the information retrieval and text
generation capabilities of LLMs to refine/enhance the topological structure of
text-attributed graphs (TAGs) under the node classification setting. First, we
propose using LLMs to help remove unreliable edges and add reliable ones in the
TAG. Specifically, we first let the LLM output the semantic similarity between
node attributes through delicate prompt designs, and then perform edge deletion
and edge addition based on the similarity. Second, we propose using
pseudo-labels generated by the LLM to improve graph topology, that is, we
introduce the pseudo-label propagation as a regularization to guide the graph
neural network (GNN) in learning proper edge weights. Finally, we incorporate
the two aforementioned LLM-based methods for graph topological refinement into
the process of GNN training, and perform extensive experiments on four
real-world datasets. The experimental results demonstrate the effectiveness of
LLM-based graph topology refinement (achieving a 0.15%--2.47% performance gain
on public benchmarks). | Artificial Intelligence |
What field is the article from? | Title: Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections
Abstract: Today's robot policies exhibit subpar performance when faced with the
challenge of generalizing to novel environments. Human corrective feedback is a
crucial form of guidance to enable such generalization. However, adapting to
and learning from online human corrections is a non-trivial endeavor: not only
do robots need to remember human feedback over time to retrieve the right
information in new settings and reduce the intervention rate, but also they
would need to be able to respond to feedback that can be arbitrary corrections
about high-level human preferences to low-level adjustments to skill
parameters. In this work, we present Distillation and Retrieval of Online
Corrections (DROC), a large language model (LLM)-based system that can respond
to arbitrary forms of language feedback, distill generalizable knowledge from
corrections, and retrieve relevant past experiences based on textual and visual
similarity for improving performance in novel settings. DROC is able to respond
to a sequence of online language corrections that address failures in both
high-level task plans and low-level skill primitives. We demonstrate that DROC
effectively distills the relevant information from the sequence of online
corrections in a knowledge base and retrieves that knowledge in settings with
new task or object instances. DROC outperforms other techniques that directly
generate robot code via LLMs by using only half of the total number of
corrections needed in the first round and requires little to no corrections
after two iterations. We show further results, videos, prompts and code on
https://sites.google.com/stanford.edu/droc . | Robotics |
What field is the article from? | Title: Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces
Abstract: The ability to identify and control different kinds of linguistic information
encoded in vector representations of words has many use cases, especially for
explainability and bias removal. This is usually done via a set of simple
classification tasks, termed probes, to evaluate the information encoded in the
embedding space. However, the involvement of a trainable classifier leads to
entanglement between the probe's results and the classifier's nature. As a
result, contemporary works on probing include tasks that do not involve
training of auxiliary models. In this work we introduce the term indicator
tasks for non-trainable tasks which are used to query embedding spaces for the
existence of certain properties, and claim that this kind of tasks may point to
a direction opposite to probes, and that this contradiction complicates the
decision on whether a property exists in an embedding space. We demonstrate our
claims with two test cases, one dealing with gender debiasing and another with
the erasure of morphological information from embedding spaces. We show that
the application of a suitable indicator provides a more accurate picture of the
information captured and removed compared to probes. We thus conclude that
indicator tasks should be implemented and taken into consideration when
eliciting information from embedded representations. | Computational Linguistics |
What field is the article from? | Title: Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data
Abstract: We study the problem of identifying the unknown intervention targets in
structural causal models where we have access to heterogeneous data collected
from multiple environments. The unknown intervention targets are the set of
endogenous variables whose corresponding exogenous noises change across the
environments. We propose a two-phase approach which in the first phase recovers
the exogenous noises corresponding to unknown intervention targets whose
distributions have changed across environments. In the second phase, the
recovered noises are matched with the corresponding endogenous variables. For
the recovery phase, we provide sufficient conditions for learning these
exogenous noises up to some component-wise invertible transformation. For the
matching phase, under the causal sufficiency assumption, we show that the
proposed method uniquely identifies the intervention targets. In the presence
of latent confounders, the intervention targets among the observed variables
cannot be determined uniquely. We provide a candidate intervention target set
which is a superset of the true intervention targets. Our approach improves
upon the state of the art as the returned candidate set is always a subset of
the target set returned by previous work. Moreover, we do not require
restrictive assumptions such as linearity of the causal model or performing
invariance tests to learn whether a distribution is changing across
environments which could be highly sample inefficient. Our experimental results
show the effectiveness of our proposed algorithm in practice. | Machine Learning |
What field is the article from? | Title: Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?
Abstract: Programming language understanding and representation (a.k.a code
representation learning) has always been a hot and challenging task in software
engineering. It aims to apply deep learning techniques to produce numerical
representations of the source code features while preserving its semantics.
These representations can be used for facilitating subsequent code-related
tasks. The abstract syntax tree (AST), a fundamental code feature, illustrates
the syntactic information of the source code and has been widely used in code
representation learning. However, there is still a lack of systematic and
quantitative evaluation of how well AST-based code representation facilitates
subsequent code-related tasks. In this paper, we first conduct a comprehensive
empirical study to explore the effectiveness of the AST-based code
representation in facilitating follow-up code-related tasks. To do so, we
compare the performance of models trained with code token sequence (Token for
short) based code representation and AST-based code representation on three
popular types of code-related tasks. Surprisingly, the overall quantitative
statistical results demonstrate that models trained with AST-based code
representation consistently perform worse across all three tasks compared to
models trained with Token-based code representation. Our further quantitative
analysis reveals that models trained with AST-based code representation
outperform models trained with Token-based code representation in certain
subsets of samples across all three tasks. We also conduct comprehensive
experiments to evaluate and reveal the impact of the choice of AST
parsing/preprocessing/encoding methods on AST-based code representation and
subsequent code-related tasks. Our study provides future researchers with
detailed guidance on how to select solutions at each stage to fully exploit
AST. | Software Engineering |
What field is the article from? | Title: Unlearning via Sparse Representations
Abstract: Machine \emph{unlearning}, which involves erasing knowledge about a
\emph{forget set} from a trained model, can prove to be costly and infeasible
by existing techniques. We propose a nearly compute-free zero-shot unlearning
technique based on a discrete representational bottleneck. We show that the
proposed technique efficiently unlearns the forget set and incurs negligible
damage to the model's performance on the rest of the data set. We evaluate the
proposed technique on the problem of \textit{class unlearning} using three
datasets: CIFAR-10, CIFAR-100, and LACUNA-100. We compare the proposed
technique to SCRUB, a state-of-the-art approach which uses knowledge
distillation for unlearning. Across all three datasets, the proposed technique
performs as well as, if not better than SCRUB while incurring almost no
computational cost. | Machine Learning |
What field is the article from? | Title: Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series
Abstract: Curriculum learning and imitation learning have been leveraged extensively in
the robotics domain. However, minimal research has been done on leveraging
these ideas on control tasks over highly stochastic time-series data. Here, we
theoretically and empirically explore these approaches in a representative
control task over complex time-series data. We implement the fundamental ideas
of curriculum learning via data augmentation, while imitation learning is
implemented via policy distillation from an oracle. Our findings reveal that
curriculum learning should be considered a novel direction in improving
control-task performance over complex time-series. Our ample random-seed
out-sample empirics and ablation studies are highly encouraging for curriculum
learning for time-series control. These findings are especially encouraging as
we tune all overlapping hyperparameters on the baseline -- giving an advantage
to the baseline. On the other hand, we find that imitation learning should be
used with caution. | Machine Learning |
What field is the article from? | Title: LOKE: Linked Open Knowledge Extraction for Automated Knowledge Graph Construction
Abstract: While the potential of Open Information Extraction (Open IE) for Knowledge
Graph Construction (KGC) may seem promising, we find that the alignment of Open
IE extraction results with existing knowledge graphs to be inadequate. The
advent of Large Language Models (LLMs), especially the commercially available
OpenAI models, have reset expectations for what is possible with deep learning
models and have created a new field called prompt engineering. We investigate
the use of GPT models and prompt engineering for knowledge graph construction
with the Wikidata knowledge graph to address a similar problem to Open IE,
which we call Open Knowledge Extraction (OKE) using an approach we call the
Linked Open Knowledge Extractor (LOKE, pronounced like "Loki"). We consider the
entity linking task essential to construction of real world knowledge graphs.
We merge the CaRB benchmark scoring approach with data from the TekGen dataset
for the LOKE task. We then show that a well engineered prompt, paired with a
naive entity linking approach (which we call LOKE-GPT), outperforms AllenAI's
OpenIE 4 implementation on the OKE task, although it over-generates triples
compared to the reference set due to overall triple scarcity in the TekGen set.
Through an analysis of entity linkability in the CaRB dataset, as well as
outputs from OpenIE 4 and LOKE-GPT, we see that LOKE-GPT and the "silver"
TekGen triples show that the task is significantly different in content from
OIE, if not structure. Through this analysis and a qualitative analysis of
sentence extractions via all methods, we found that LOKE-GPT extractions are of
high utility for the KGC task and suitable for use in semi-automated extraction
settings. | Computational Linguistics |
What field is the article from? | Title: FloodBrain: Flood Disaster Reporting by Web-based Retrieval Augmented Generation with an LLM
Abstract: Fast disaster impact reporting is crucial in planning humanitarian
assistance. Large Language Models (LLMs) are well known for their ability to
write coherent text and fulfill a variety of tasks relevant to impact
reporting, such as question answering or text summarization. However, LLMs are
constrained by the knowledge within their training data and are prone to
generating inaccurate, or "hallucinated", information. To address this, we
introduce a sophisticated pipeline embodied in our tool FloodBrain
(floodbrain.com), specialized in generating flood disaster impact reports by
extracting and curating information from the web. Our pipeline assimilates
information from web search results to produce detailed and accurate reports on
flood events. We test different LLMs as backbones in our tool and compare their
generated reports to human-written reports on different metrics. Similar to
other studies, we find a notable correlation between the scores assigned by
GPT-4 and the scores given by human evaluators when comparing our generated
reports to human-authored ones. Additionally, we conduct an ablation study to
test our single pipeline components and their relevancy for the final reports.
With our tool, we aim to advance the use of LLMs for disaster impact reporting
and reduce the time for coordination of humanitarian efforts in the wake of
flood disasters. | Artificial Intelligence |
What field is the article from? | Title: Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing
Abstract: In this paper, we address the issue of increasing the performance of
reinforcement learning (RL) solutions for autonomous racing cars when
navigating under conditions where practical vehicle modelling errors (commonly
known as \emph{model mismatches}) are present. To address this challenge, we
propose a partial end-to-end algorithm that decouples the planning and control
tasks. Within this framework, an RL agent generates a trajectory comprising a
path and velocity, which is subsequently tracked using a pure pursuit steering
controller and a proportional velocity controller, respectively. In contrast,
many current learning-based (i.e., reinforcement and imitation learning)
algorithms utilise an end-to-end approach whereby a deep neural network
directly maps from sensor data to control commands. By leveraging the
robustness of a classical controller, our partial end-to-end driving algorithm
exhibits better robustness towards model mismatches than standard end-to-end
algorithms. | Robotics |
What field is the article from? | Title: On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
Abstract: Contemporary machine learning requires training large neural networks on
massive datasets and thus faces the challenges of high computational demands.
Dataset distillation, as a recent emerging strategy, aims to compress
real-world datasets for efficient training. However, this line of research
currently struggle with large-scale and high-resolution datasets, hindering its
practicality and feasibility. To this end, we re-examine the existing dataset
distillation methods and identify three properties required for large-scale
real-world applications, namely, realism, diversity, and efficiency. As a
remedy, we propose RDED, a novel computationally-efficient yet effective data
distillation paradigm, to enable both diversity and realism of the distilled
data. Extensive empirical results over various neural architectures and
datasets demonstrate the advancement of RDED: we can distill the full
ImageNet-1K to a small dataset comprising 10 images per class within 7 minutes,
achieving a notable 42% top-1 accuracy with ResNet-18 on a single RTX-4090 GPU
(while the SOTA only achieves 21% but requires 6 hours). | Computer Vision |
What field is the article from? | Title: A Language and Its Dimensions: Intrinsic Dimensions of Language Fractal Structures
Abstract: The present paper introduces a novel object of study - a language fractal
structure. We hypothesize that a set of embeddings of all $n$-grams of a
natural language constitutes a representative sample of this fractal set. (We
use the term Hailonakea to refer to the sum total of all language fractal
structures, over all $n$). The paper estimates intrinsic (genuine) dimensions
of language fractal structures for the Russian and English languages. To this
end, we employ methods based on (1) topological data analysis and (2) a minimum
spanning tree of a data graph for a cloud of points considered (Steele
theorem). For both languages, for all $n$, the intrinsic dimensions appear to
be non-integer values (typical for fractal sets), close to 9 for both of the
Russian and English language. | Computational Linguistics |
What field is the article from? | Title: Token Recycling for Efficient Sequential Inference with Vision Transformers
Abstract: Vision Transformers (ViTs) overpass Convolutional Neural Networks in
processing incomplete inputs because they do not require the imputation of
missing values. Therefore, ViTs are well suited for sequential decision-making,
e.g. in the Active Visual Exploration problem. However, they are
computationally inefficient because they perform a full forward pass each time
a piece of new sequential information arrives.
To reduce this computational inefficiency, we introduce the TOken REcycling
(TORE) modification for the ViT inference, which can be used with any
architecture. TORE divides ViT into two parts, iterator and aggregator. An
iterator processes sequential information separately into midway tokens, which
are cached. The aggregator processes midway tokens jointly to obtain the
prediction. This way, we can reuse the results of computations made by
iterator.
Except for efficient sequential inference, we propose a complementary
training policy, which significantly reduces the computational burden
associated with sequential decision-making while achieving state-of-the-art
accuracy. | Machine Learning |
What field is the article from? | Title: A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN
Abstract: Accurate Defect detection is crucial for ensuring the trustworthiness of
intelligent railway systems. Current approaches rely on single deep-learning
models, like CNNs, which employ a large amount of data to capture underlying
patterns. Training a new defect classifier with limited samples often leads to
overfitting and poor performance on unseen images. To address this, researchers
have advocated transfer learning and fine-tuning the pre-trained models.
However, using a single backbone network in transfer learning still may cause
bottleneck issues and inconsistent performance if it is not suitable for a
specific problem domain. To overcome these challenges, we propose a reusable
AI-enabled defect detection approach. By combining ensemble learning with
transfer learning models (VGG-19, MobileNetV3, and ResNet-50), we improved the
classification accuracy and achieved consistent performance at a certain phase
of training. Our empirical analysis demonstrates better and more consistent
performance compared to other state-of-the-art approaches. The consistency
substantiates the reusability of the defect detection system for newly evolved
defected rail parts. Therefore we anticipate these findings to benefit further
research and development of reusable AI-enabled solutions for railway systems. | Computer Vision |
What field is the article from? | Title: Class-Aware Pruning for Efficient Neural Networks
Abstract: Deep neural networks (DNNs) have demonstrated remarkable success in various
fields. However, the large number of floating-point operations (FLOPs) in DNNs
poses challenges for their deployment in resource-constrained applications,
e.g., edge devices. To address the problem, pruning has been introduced to
reduce the computational cost in executing DNNs. Previous pruning strategies
are based on weight values, gradient values and activation outputs. Different
from previous pruning solutions, in this paper, we propose a class-aware
pruning technique to compress DNNs, which provides a novel perspective to
reduce the computational cost of DNNs. In each iteration, the neural network
training is modified to facilitate the class-aware pruning. Afterwards, the
importance of filters with respect to the number of classes is evaluated. The
filters that are only important for a few number of classes are removed. The
neural network is then retrained to compensate for the incurred accuracy loss.
The pruning iterations end until no filter can be removed anymore, indicating
that the remaining filters are very important for many classes. This pruning
technique outperforms previous pruning solutions in terms of accuracy, pruning
ratio and the reduction of FLOPs. Experimental results confirm that this
class-aware pruning technique can significantly reduce the number of weights
and FLOPs, while maintaining a high inference accuracy. | Artificial Intelligence |
What field is the article from? | Title: Implicit Chain of Thought Reasoning via Knowledge Distillation
Abstract: To augment language models with the ability to reason, researchers usually
prompt or finetune them to produce chain of thought reasoning steps before
producing the final answer. However, although people use natural language to
reason effectively, it may be that LMs could reason more effectively with some
intermediate computation that is not in natural language. In this work, we
explore an alternative reasoning approach: instead of explicitly producing the
chain of thought reasoning steps, we use the language model's internal hidden
states to perform implicit reasoning. The implicit reasoning steps are
distilled from a teacher model trained on explicit chain-of-thought reasoning,
and instead of doing reasoning "horizontally" by producing intermediate words
one-by-one, we distill it such that the reasoning happens "vertically" among
the hidden states in different layers. We conduct experiments on a multi-digit
multiplication task and a grade school math problem dataset and find that this
approach enables solving tasks previously not solvable without explicit
chain-of-thought, at a speed comparable to no chain-of-thought. | Computational Linguistics |
What field is the article from? | Title: Using Analytics on Student Created Data to Content Validate Pedagogical Tools
Abstract: Conceptual and simulation models can function as useful pedagogical tools,
however it is important to categorize different outcomes when evaluating them
in order to more meaningfully interpret results. VERA is a ecology-based
conceptual modeling software that enables users to simulate interactions
between biotics and abiotics in an ecosystem, allowing users to form and then
verify hypothesis through observing a time series of the species populations.
In this paper, we classify this time series into common patterns found in the
domain of ecological modeling through two methods, hierarchical clustering and
curve fitting, illustrating a general methodology for showing content validity
when combining different pedagogical tools. When applied to a diverse sample of
263 models containing 971 time series collected from three different VERA user
categories: a Georgia Tech (GATECH), North Georgia Technical College (NGTC),
and ``Self Directed Learners'', results showed agreement between both
classification methods on 89.38\% of the sample curves in the test set. This
serves as a good indication that our methodology for determining content
validity was successful. | Artificial Intelligence |
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