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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 ... | 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
with... | 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 e... | 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),
specif... | 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 ... | 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 appeali... | 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 ... | 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 ... | 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 Eng... | 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, au... | 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 envir... | 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... | 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
... | 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 M... | 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... | 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 c... | 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 ch... | 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 st... | 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... | 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-sou... | 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 ... | 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... | 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... | 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 ... | 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 person... | 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 coord... | 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
b... | 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 result... | 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... | 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... | 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 ... | 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... | 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 Proce... | 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 ... | 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 ... | 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 an... | 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 stakeholde... | 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
univa... | 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... | 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 framewo... | 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... | 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 ... | 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... | 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 bou... | 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 t... | 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 stora... | 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 generat... | 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 signifi... | 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 ... | 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... | 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 knowled... | 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 signif... | 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... | 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, y... | 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 st... | 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) techn... | 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 ac... | 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 p... | 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
res... | 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... | 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 environmen... | 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 l... | 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 ... | 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... | 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 e... | 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,... | 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
environme... | 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 reasonin... | 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-maki... | 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-de... | 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
t... | 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
comp... | 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 attribute... | 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 ... | 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 abilitie... | 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 ... | 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 l... | 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 sh... | 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... | 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... | 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 l... | 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 r... | 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 sing... | 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 pote... | 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.... | 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 ... | 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 ar... | 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 pro... | 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 representat... | 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-se... | 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 inadequ... | 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 imp... | 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 ... | 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 ... | 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.... | 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... | 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 underlyin... | 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... | 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 effect... | 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... | Artificial Intelligence |
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