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What field is the article from? | Title: Optimizing Dense Feed-Forward Neural Networks
Abstract: Deep learning models have been widely used during the last decade due to
their outstanding learning and abstraction capacities. However, one of the main
challenges any scientist has to face using deep learning models is to establish
the network's architectu... | Machine Learning |
What field is the article from? | Title: "It's not like Jarvis, but it's pretty close!" -- Examining ChatGPT's Usage among Undergraduate Students in Computer Science
Abstract: Large language models (LLMs) such as ChatGPT and Google Bard have garnered
significant attention in the academic community. Previous research has
evaluated these LLMs for various... | Human-Computer Interaction |
What field is the article from? | Title: From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems
Abstract: Encoding legislative text in a formal representation is an important
prerequisite to different tasks in the field of AI & Law. For example,
rule-based expert systems focused on legislation can support... | Computational Linguistics |
What field is the article from? | Title: A DRL solution to help reduce the cost in waiting time of securing a traffic light for cyclists
Abstract: Cyclists prefer to use infrastructure that separates them from motorized
traffic. Using a traffic light to segregate car and bike flows, with the
addition of bike-specific green phases, is a lightweight and ... | Artificial Intelligence |
What field is the article from? | Title: Coupling Fairness and Pruning in a Single Run: a Bi-level Optimization Perspective
Abstract: Deep neural networks have demonstrated remarkable performance in various
tasks. With a growing need for sparse deep learning, model compression
techniques, especially pruning, have gained significant attention. However,
... | Machine Learning |
What field is the article from? | Title: TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise
Abstract: Large Language Models (LLMs) exhibit impressive reasoning and data
augmentation capabilities in various NLP tasks. However, what about small
models? In this work, we propose TeacherLM-7.1B, capable of annotating relevan... | Computational Linguistics |
What field is the article from? | Title: Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model
Abstract: Efficiently modeling spatio-temporal (ST) physical processes and observations
presents a challenging problem for the deep learning community. Many recent
studies have concentrated on meticulously reconciling various advanta... | Artificial Intelligence |
What field is the article from? | Title: FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment
Abstract: Deep learning techniques have greatly enhanced the performance of fire
detection in videos. However, video-based fire detection models heavily rely on
labeled data, and the process of data labeling... | Computer Vision |
What field is the article from? | Title: A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases
Abstract: Enterprise applications of Large Language Models (LLMs) hold promise for
question answering on enterprise SQL databases. However, the extent to which
LLMs can acc... | Artificial Intelligence |
What field is the article from? | Title: A Path to Simpler Models Starts With Noise
Abstract: The Rashomon set is the set of models that perform approximately equally well
on a given dataset, and the Rashomon ratio is the fraction of all models in a
given hypothesis space that are in the Rashomon set. Rashomon ratios are often
large for tabular dataset... | Machine Learning |
What field is the article from? | Title: Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flows
Abstract: Designing predictive models for subjective problems in natural language
processing (NLP) remains challenging. This is mainly due to its
non-deterministic nature and different perceptions of the content by different
humans. I... | Artificial Intelligence |
What field is the article from? | Title: Leveraging Reinforcement Learning and Large Language Models for Code Optimization
Abstract: Code optimization is a daunting task that requires a significant level of
expertise from experienced programmers. This level of expertise is not
sufficient when compared to the rapid development of new hardware
architectu... | Machine Learning |
What field is the article from? | Title: Unified Segment-to-Segment Framework for Simultaneous Sequence Generation
Abstract: Simultaneous sequence generation is a pivotal task for real-time scenarios,
such as streaming speech recognition, simultaneous machine translation and
simultaneous speech translation, where the target sequence is generated while
... | Computational Linguistics |
What field is the article from? | Title: Real Customization or Just Marketing: Are Customized Versions of Chat GPT Useful?
Abstract: Large Language Models (LLMs), as the case of OpenAI ChatGPT-4 Turbo, are
revolutionizing several industries, including higher education. In this
context, LLMs can be personalized through a fine-tuning process to meet the
... | Computational Linguistics |
What field is the article from? | Title: The Role of Chain-of-Thought in Complex Vision-Language Reasoning Task
Abstract: The study explores the effectiveness of the Chain-of-Thought approach, known
for its proficiency in language tasks by breaking them down into sub-tasks and
intermediate steps, in improving vision-language tasks that demand
sophistic... | Computational Linguistics |
What field is the article from? | Title: LuminanceL1Loss: A loss function which measures percieved brightness and colour differences
Abstract: We introduce LuminanceL1Loss, a novel loss function designed to enhance the
performance of image restoration tasks. We demonstrate its superiority over MSE
when applied to the Retinexformer, BUIFD and DnCNN arch... | Computer Vision |
What field is the article from? | Title: Symbolic Planning and Code Generation for Grounded Dialogue
Abstract: Large language models (LLMs) excel at processing and generating both text and
code. However, LLMs have had limited applicability in grounded task-oriented
dialogue as they are difficult to steer toward task objectives and fail to
handle novel ... | Computational Linguistics |
What field is the article from? | Title: A multi-sourced data and agent-based approach for complementing Time Use Surveys in the context of residential human activity and load curve simulation
Abstract: To address the major issues associated with using Time-Use Survey (TUS) for
simulating residential load curves, we present the SMACH approach, which
co... | Artificial Intelligence |
What field is the article from? | Title: Can Physics Informed Neural Operators Self Improve?
Abstract: Self-training techniques have shown remarkable value across many deep
learning models and tasks. However, such techniques remain largely unexplored
when considered in the context of learning fast solvers for systems of partial
differential equations (... | Machine Learning |
What field is the article from? | Title: FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models
Abstract: We introduce FaceTalk, a novel generative approach designed for synthesizing
high-fidelity 3D motion sequences of talking human heads from input audio
signal. To capture the expressive, detailed nature of human heads, including
h... | Computer Vision |
What field is the article from? | Title: Can GPT models Follow Human Summarization Guidelines? Evaluating ChatGPT and GPT-4 for Dialogue Summarization
Abstract: This study explores the capabilities of prompt-driven Large Language Models
(LLMs) like ChatGPT and GPT-4 in adhering to human guidelines for dialogue
summarization. Experiments employed Dialog... | Computational Linguistics |
What field is the article from? | Title: In-Context Learning for Knowledge Base Question Answering for Unmanned Systems based on Large Language Models
Abstract: Knowledge Base Question Answering (KBQA) aims to answer factoid questions
based on knowledge bases. However, generating the most appropriate knowledge
base query code based on Natural Language ... | Computational Linguistics |
What field is the article from? | Title: YOLO-BEV: Generating Bird's-Eye View in the Same Way as 2D Object Detection
Abstract: Vehicle perception systems strive to achieve comprehensive and rapid visual
interpretation of their surroundings for improved safety and navigation. We
introduce YOLO-BEV, an efficient framework that harnesses a unique surround... | Computer Vision |
What field is the article from? | Title: MathNAS: If Blocks Have a Role in Mathematical Architecture Design
Abstract: Neural Architecture Search (NAS) has emerged as a favoured method for
unearthing effective neural architectures. Recent development of large models
has intensified the demand for faster search speeds and more accurate search
results. Ho... | Machine Learning |
What field is the article from? | Title: Personalized Decision Supports based on Theory of Mind Modeling and Explainable Reinforcement Learning
Abstract: In this paper, we propose a novel personalized decision support system that
combines Theory of Mind (ToM) modeling and explainable Reinforcement Learning
(XRL) to provide effective and interpretable i... | Machine Learning |
What field is the article from? | Title: Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization
Abstract: In Reinforcement Learning (RL), agents have no incentive to exhibit
predictable behaviors, and are often pushed (through e.g. policy entropy
regularization) to randomize their actions in favor of exploration. From a
human per... | Machine Learning |
What field is the article from? | Title: MMM: Generative Masked Motion Model
Abstract: Recent advances in text-to-motion generation using diffusion and
autoregressive models have shown promising results. However, these models often
suffer from a trade-off between real-time performance, high fidelity, and
motion editability. To address this gap, we intr... | Computer Vision |
What field is the article from? | Title: ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation
Abstract: Large language models have shown good performances in generating code to meet
human requirements. However, human requirements expressed in natural languages
can be vague, incomplete, and ambiguous, leading large language models to
... | Software Engineering |
What field is the article from? | Title: IL-NeRF: Incremental Learning for Neural Radiance Fields with Camera Pose Alignment
Abstract: Neural radiance fields (NeRF) is a promising approach for generating
photorealistic images and representing complex scenes. However, when processing
data sequentially, it can suffer from catastrophic forgetting, where p... | Computer Vision |
What field is the article from? | Title: Unified learning-based lossy and lossless JPEG recompression
Abstract: JPEG is still the most widely used image compression algorithm. Most image
compression algorithms only consider uncompressed original image, while
ignoring a large number of already existing JPEG images. Recently, JPEG
recompression approache... | Computer Vision |
What field is the article from? | Title: LLMs may Dominate Information Access: Neural Retrievers are Biased Towards LLM-Generated Texts
Abstract: Recently, the emergence of large language models (LLMs) has revolutionized
the paradigm of information retrieval (IR) applications, especially in web
search. With their remarkable capabilities in generating h... | Information Retrieval |
What field is the article from? | Title: SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers
Abstract: Vision-based human-to-robot handover is an important and challenging task in
human-robot interaction. Recent work has attempted to train robot policies by
interacting with dynamic virtual humans in simulated environments, wh... | Robotics |
What field is the article from? | Title: GPQA: A Graduate-Level Google-Proof Q&A Benchmark
Abstract: We present GPQA, a challenging dataset of 448 multiple-choice questions
written by domain experts in biology, physics, and chemistry. We ensure that
the questions are high-quality and extremely difficult: experts who have or are
pursuing PhDs in the cor... | Artificial Intelligence |
What field is the article from? | Title: Amortized Bayesian Decision Making for simulation-based models
Abstract: Simulation-based inference (SBI) provides a powerful framework for inferring
posterior distributions of stochastic simulators in a wide range of domains. In
many settings, however, the posterior distribution is not the end goal itself
-- ra... | Machine Learning |
What field is the article from? | Title: Graph Information Bottleneck for Remote Sensing Segmentation
Abstract: Remote sensing segmentation has a wide range of applications in environmental
protection, and urban change detection, etc. Despite the success of deep
learning-based remote sensing segmentation methods (e.g., CNN and Transformer),
they are no... | Computer Vision |
What field is the article from? | Title: Large Language Models Meet Computer Vision: A Brief Survey
Abstract: Recently, the intersection of Large Language Models (LLMs) and Computer
Vision (CV) has emerged as a pivotal area of research, driving significant
advancements in the field of Artificial Intelligence (AI). As transformers have
become the backbo... | Computer Vision |
What field is the article from? | Title: Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation
Abstract: This paper investigates Cross-Domain Sequential Recommendation (CDSR), a
promising method that uses information from multiple domains (more than three)
to generate accurate and diver... | Artificial Intelligence |
What field is the article from? | Title: Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
Abstract: Hybrid RL is the setting where an RL agent has access to both offline data
and online data by interacting with the real-world environment. In this work,
we propose a new hybrid RL algorithm that combines an on-policy actor-critic
... | Machine Learning |
What field is the article from? | Title: BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering
Abstract: Medical Visual Question Answering (Med-VQA) is a very important task in
healthcare industry, which answers a natural language question with a medical
image. Existing VQA techniques in information systems can be directly appli... | Artificial Intelligence |
What field is the article from? | Title: Reframing Audience Expansion through the Lens of Probability Density Estimation
Abstract: Audience expansion has become an important element of prospective marketing,
helping marketers create target audiences based on a mere representative sample
of their current customer base. Within the realm of machine learni... | Artificial Intelligence |
What field is the article from? | Title: Enhancing Human Persuasion With Large Language Models
Abstract: Although large language models (LLMs) are reshaping various aspects of human
life, our current understanding of their impacts remains somewhat constrained.
Here we investigate the impact of LLMs on human communication, in the context
of consumer com... | Human-Computer Interaction |
What field is the article from? | Title: Correlated Attention in Transformers for Multivariate Time Series
Abstract: Multivariate time series (MTS) analysis prevails in real-world applications
such as finance, climate science and healthcare. The various self-attention
mechanisms, the backbone of the state-of-the-art Transformer-based models,
efficientl... | Machine Learning |
What field is the article from? | Title: Causal Optimal Transport of Abstractions
Abstract: Causal abstraction (CA) theory establishes formal criteria for relating
multiple structural causal models (SCMs) at different levels of granularity by
defining maps between them. These maps have significant relevance for
real-world challenges such as synthesizin... | Machine Learning |
What field is the article from? | Title: GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer
Abstract: Named Entity Recognition (NER) is essential in various Natural Language
Processing (NLP) applications. Traditional NER models are effective but limited
to a set of predefined entity types. In contrast, Large Language ... | Computational Linguistics |
What field is the article from? | Title: ChatGPT as Co-Advisor in Scientific Initiation: Action Research with Project-Based Learning in Elementary Education
Abstract: Background: In the contemporary educational landscape, technology has the
power to drive innovative pedagogical practices. Overcoming the resistance of
teachers and students to adopting n... | Computers and Society |
What field is the article from? | Title: Flames: Benchmarking Value Alignment of Chinese Large Language Models
Abstract: The widespread adoption of large language models (LLMs) across various
regions underscores the urgent need to evaluate their alignment with human
values. Current benchmarks, however, fall short of effectively uncovering
safety vulner... | Computational Linguistics |
What field is the article from? | Title: A Decision Support System for Liver Diseases Prediction: Integrating Batch Processing, Rule-Based Event Detection and SPARQL Query
Abstract: Liver diseases pose a significant global health burden, impacting a
substantial number of individuals and exerting substantial economic and social
consequences. Rising live... | Artificial Intelligence |
What field is the article from? | Title: A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning
Abstract: Logical reasoning has been an ongoing pursuit in the field of AI. Despite
significant advancements made by large language models (LLMs), they still
struggle with complex logical reasoning problems. To enhance... | Artificial Intelligence |
What field is the article from? | Title: PEFTDebias : Capturing debiasing information using PEFTs
Abstract: The increasing use of foundation models highlights the urgent need to address
and eliminate implicit biases present in them that arise during pretraining. In
this paper, we introduce PEFTDebias, a novel approach that employs
parameter-efficient f... | Machine Learning |
What field is the article from? | Title: DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding
Abstract: Traffic accidents frequently lead to fatal injuries, contributing to over 50
million deaths until 2023. To mitigate driving hazards and ensure personal
safety, it is crucial to assist vehicles... | Computer Vision |
What field is the article from? | Title: Artificial intelligence optical hardware empowers high-resolution hyperspectral video understanding at 1.2 Tb/s
Abstract: Foundation models, exemplified by GPT technology, are discovering new
horizons in artificial intelligence by executing tasks beyond their designers'
expectations. While the present generation... | Computer Vision |
What field is the article from? | Title: How do Language Models Bind Entities in Context?
Abstract: To correctly use in-context information, language models (LMs) must bind
entities to their attributes. For example, given a context describing a "green
square" and a "blue circle", LMs must bind the shapes to their respective
colors. We analyze LM repres... | Machine Learning |
What field is the article from? | Title: Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games
Abstract: Video games have served as useful benchmarks for the decision making
community, but going beyond Atari games towards training agents in modern games
has been prohibitively expensive for the vast majority of the research
communi... | Machine Learning |
What field is the article from? | Title: Improving a Named Entity Recognizer Trained on Noisy Data with a Few Clean Instances
Abstract: To achieve state-of-the-art performance, one still needs to train NER models
on large-scale, high-quality annotated data, an asset that is both costly and
time-intensive to accumulate. In contrast, real-world applicati... | Computational Linguistics |
What field is the article from? | Title: ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair
Abstract: With the growing interest on Large Language Models (LLMs) for fault
localization and program repair, ensuring the integrity and generalizability of
the LLM-based methods becomes paramount. ... | Software Engineering |
What field is the article from? | Title: RigLSTM: Recurrent Independent Grid LSTM for Generalizable Sequence Learning
Abstract: Sequential processes in real-world often carry a combination of simple
subsystems that interact with each other in certain forms. Learning such a
modular structure can often improve the robustness against environmental
changes... | Machine Learning |
What field is the article from? | Title: RIGA: A Regret-Based Interactive Genetic Algorithm
Abstract: In this paper, we propose an interactive genetic algorithm for solving
multi-objective combinatorial optimization problems under preference
imprecision. More precisely, we consider problems where the decision maker's
preferences over solutions can be r... | Artificial Intelligence |
What field is the article from? | Title: GROOViST: A Metric for Grounding Objects in Visual Storytelling
Abstract: A proper evaluation of stories generated for a sequence of images -- the task
commonly referred to as visual storytelling -- must consider multiple aspects,
such as coherence, grammatical correctness, and visual grounding. In this work,
we... | Artificial Intelligence |
What field is the article from? | Title: E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation
Abstract: The recent advancements in Large Language Models (LLMs) have sparked interest
in harnessing their potential within recommender systems. Since LLMs are
designed for natural language tasks, e... | Information Retrieval |
What field is the article from? | Title: GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling
Abstract: Linear Recurrence has proven to be a powerful tool for modeling long
sequences efficiently. In this work, we show that existing models fail to take
full advantage of its potential. Motivated by this finding, we develop
GateLoop, a ... | Machine Learning |
What field is the article from? | Title: Understanding and Mitigating Classification Errors Through Interpretable Token Patterns
Abstract: State-of-the-art NLP methods achieve human-like performance on many tasks,
but make errors nevertheless. Characterizing these errors in easily
interpretable terms gives insight into whether a classifier is prone to ... | Computational Linguistics |
What field is the article from? | Title: GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization
Abstract: Text summarization (TS) is a natural language processing (NLP) subtask
pertaining to the automatic formulation of a concise and coherent summary that
covers the major concepts and topics from one or multiple documents. Recent... | Computational Linguistics |
What field is the article from? | Title: DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries
Abstract: Delivery of items from the producer to the consumer has experienced
significant growth over the past decade and has been greatly fueled by the
recent pandemic. Amazon Fresh, Shopify, UberEats, InstaCart, and Do... | Machine Learning |
What field is the article from? | Title: RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis
Abstract: Aspect-based sentiment analysis (ABSA) is dedicated to forecasting the
sentiment polarity of aspect terms within sentences. Employing graph neural
networks to capture structural patterns from syntactic dependen... | Computational Linguistics |
What field is the article from? | Title: Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ?
Abstract: For specialized and dense downstream tasks such as object detection, labeling
data requires expertise and can be very expensive, making few-shot and
semi-supervised models much more attractive alternativ... | Computer Vision |
What field is the article from? | Title: On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection
Abstract: Successful detection of Out-of-Distribution (OoD) data is becoming
increasingly important to ensure safe deployment of neural networks. One of the
main challenges in OoD detection is that neural networks output overconfident
predi... | Computer Vision |
What field is the article from? | Title: Learned Causal Method Prediction
Abstract: For a given causal question, it is important to efficiently decide which
causal inference method to use for a given dataset. This is challenging because
causal methods typically rely on complex and difficult-to-verify assumptions,
and cross-validation is not applicable ... | Machine Learning |
What field is the article from? | Title: Students' interest in knowledge acquisition in Artificial Intelligence
Abstract: Some students' expectations and points of view related to the Artificial
Intelligence course are explored and analyzed in this study. We anonymous
collected answers from 58 undergraduate students out of 200 enrolled in the
Computer ... | Computers and Society |
What field is the article from? | Title: Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object Discovery
Abstract: Besides performance, efficiency is a key design driver of technologies
supporting vehicular perception. Indeed, a well-balanced trade-off... | Computer Vision |
What field is the article from? | Title: Time Series Anomaly Detection using Diffusion-based Models
Abstract: Diffusion models have been recently used for anomaly detection (AD) in
images. In this paper we investigate whether they can also be leveraged for AD
on multivariate time series (MTS). We test two diffusion-based models and
compare them to seve... | Machine Learning |
What field is the article from? | Title: Automated Fact-Checking in Dialogue: Are Specialized Models Needed?
Abstract: Prior research has shown that typical fact-checking models for stand-alone
claims struggle with claims made in dialogues. As a solution, fine-tuning these
models on labelled dialogue data has been proposed. However, creating separate
m... | Computational Linguistics |
What field is the article from? | Title: Dig-CSI: A Distributed and Generative Model Assisted CSI Feedback Training Framework
Abstract: The advent of deep learning (DL)-based models has significantly advanced
Channel State Information (CSI) feedback mechanisms in wireless communication
systems. However, traditional approaches often suffer from high com... | Artificial Intelligence |
What field is the article from? | Title: How should the advent of large language models affect the practice of science?
Abstract: Large language models (LLMs) are being increasingly incorporated into
scientific workflows. However, we have yet to fully grasp the implications of
this integration. How should the advent of large language models affect the
... | Computational Linguistics |
What field is the article from? | Title: ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints
Abstract: Ship design is a years-long process that requires balancing complex design
trade-offs to create a ship that is efficient and effective. Finding new ways
to improve the ship design process can lead to... | Machine Learning |
What field is the article from? | Title: Market Concentration Implications of Foundation Models
Abstract: We analyze the structure of the market for foundation models, i.e., large AI
models such as those that power ChatGPT and that are adaptable to downstream
uses, and we examine the implications for competition policy and regulation. We
observe that t... | Artificial Intelligence |
What field is the article from? | Title: DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase
Abstract: In-Context Learning (ICL) combined with pre-trained large language models has
achieved promising results on various NLP tasks. However, ICL requires
high-quality annotated demonstrations which might not be available in
real-world scena... | Computational Linguistics |
What field is the article from? | Title: $σ$-PCA: a unified neural model for linear and nonlinear principal component analysis
Abstract: Linear principal component analysis (PCA), nonlinear PCA, and linear
independent component analysis (ICA) -- those are three methods with
single-layer autoencoder formulations for learning linear transformations from
... | Machine Learning |
What field is the article from? | Title: Modeling the Uncertainty with Maximum Discrepant Students for Semi-supervised 2D Pose Estimation
Abstract: Semi-supervised pose estimation is a practically challenging task for
computer vision. Although numerous excellent semi-supervised classification
methods have emerged, these methods typically use confidence... | Computer Vision |
What field is the article from? | Title: Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation
Abstract: Evaluating text-to-image models is notoriously difficult. A strong recent
approach for assessing text-image faithfulness is based on QG/A (question
generation and answering), which uses pre-trained fo... | Computer Vision |
What field is the article from? | Title: DEFT: Data Efficient Fine-Tuning for Large Language Models via Unsupervised Core-Set Selection
Abstract: Recent advances have led to the availability of many pre-trained language
models (PLMs); however, a question that remains is how much data is truly
needed to fine-tune PLMs for downstream tasks? In this work,... | Computational Linguistics |
What field is the article from? | Title: Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
Abstract: The recent explosion in the capabilities of large language models has led to
a wave of interest in how best to prompt a model to perform a given task. While
it may be tempting to simply choose a prompt based o... | Machine Learning |
What field is the article from? | Title: Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations
Abstract: Ensuring both transparency and safety is critical when deploying Deep Neural
Networks (DNNs) in high-risk applications, such as medicine. The field of
explainable AI (XAI) has proposed vario... | Computer Vision |
What field is the article from? | Title: Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes
Abstract: The task of deepfakes detection is far from being solved by speech or vision
researchers. Several publicly available databases of fake synthetic video and
speech were built to aid the development of detection methods. However,
ex... | Computer Vision |
What field is the article from? | Title: VLTSeg: Simple Transfer of CLIP-Based Vision-Language Representations for Domain Generalized Semantic Segmentation
Abstract: Domain generalization (DG) remains a significant challenge for perception
based on deep neural networks (DNN), where domain shifts occur due to lighting,
weather, or geolocation changes. I... | Computer Vision |
What field is the article from? | Title: Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data
Abstract: In todays competitive landscape, businesses grapple with customer retention.
Churn prediction models, although beneficial, often lack accuracy due to the
reliance on a single data source.... | Machine Learning |
What field is the article from? | Title: XplainLLM: A QA Explanation Dataset for Understanding LLM Decision-Making
Abstract: Large Language Models (LLMs) have recently made impressive strides in natural
language understanding tasks. Despite their remarkable performance,
understanding their decision-making process remains a big challenge. In this
paper,... | Computational Linguistics |
What field is the article from? | Title: Using GPT-4 to Augment Unbalanced Data for Automatic Scoring
Abstract: Machine learning-based automatic scoring can be challenging if students'
responses are unbalanced across scoring categories, as it introduces
uncertainty in the machine training process. To meet this challenge, we
introduce a novel text data ... | Computational Linguistics |
What field is the article from? | Title: ProAgent: From Robotic Process Automation to Agentic Process Automation
Abstract: From ancient water wheels to robotic process automation (RPA), automation
technology has evolved throughout history to liberate human beings from arduous
tasks. Yet, RPA struggles with tasks needing human-like intelligence,
especia... | Robotics |
What field is the article from? | Title: SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action Segmentation
Abstract: Multi-modal human action segmentation is a critical and challenging task with
a wide range of applications. Nowadays, the majority of approaches concentrate
on the fusion of dense signals (i.e., RGB, optical flow, and... | Computer Vision |
What field is the article from? | Title: Deep Natural Language Feature Learning for Interpretable Prediction
Abstract: We propose a general method to break down a main complex task into a set of
intermediary easier sub-tasks, which are formulated in natural language as
binary questions related to the final target task. Our method allows for
representin... | Computational Linguistics |
What field is the article from? | Title: The Expressive Power of Low-Rank Adaptation
Abstract: Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method that
leverages low-rank adaptation of weight matrices, has emerged as a prevalent
technique for fine-tuning pre-trained models such as large language models and
diffusion models. Despite its... | Machine Learning |
What field is the article from? | Title: Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning
Abstract: Federated learning (FL) emphasizes decentralized training by storing data
locally and sending only model updates, underlining user privacy. Recently, a
line of works on privacy attacks... | Machine Learning |
What field is the article from? | Title: Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs
Abstract: This paper presents an in-depth study of multimodal machine translation
(MMT), examining the prevailing understanding that MMT systems exhibit
decreased sensitivity to visual information when text inpu... | Computational Linguistics |
What field is the article from? | Title: SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models
Abstract: A new method called the Survival Beran-based Neural Importance Model
(SurvBeNIM) is proposed. It aims to explain predictions of machine learning
survival models, which are in the form of survival or cumulative hazard
... | Machine Learning |
What field is the article from? | Title: Search-Based Fairness Testing: An Overview
Abstract: Artificial Intelligence (AI) has demonstrated remarkable capabilities in
domains such as recruitment, finance, healthcare, and the judiciary. However,
biases in AI systems raise ethical and societal concerns, emphasizing the need
for effective fairness testing... | Artificial Intelligence |
What field is the article from? | Title: Vertical Federated Alzheimer's Detection on Multimodal Data
Abstract: In the era of rapidly advancing medical technologies, the segmentation of
medical data has become inevitable, necessitating the development of privacy
preserving machine learning algorithms that can train on distributed data.
Consolidating sen... | Machine Learning |
What field is the article from? | Title: Algorithmic Transparency and Manipulation
Abstract: A series of recent papers raises worries about the manipulative potential of
algorithmic transparency. But while the concern is apt and relevant, it is
based on a fraught understanding of manipulation. Therefore, this paper draws
attention to the indifference v... | Artificial Intelligence |
What field is the article from? | Title: Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts
Abstract: Graph neural networks (GNNs) have led to major breakthroughs in a variety of
domains such as drug discovery, social network analysis, and travel time
estimation. However, they lack interpretability which hinders human trust and
... | Machine Learning |
What field is the article from? | Title: Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning
Abstract: In maritime traffic surveillance, detecting illegal activities, such as
illegal fishing or transshipment of illicit products is a crucial task of the
coastal administration. In the open sea, one has... | Machine Learning |
What field is the article from? | Title: Improving search relevance of Azure Cognitive Search by Bayesian optimization
Abstract: Azure Cognitive Search (ACS) has emerged as a major contender in "Search as a
Service" cloud products in recent years. However, one of the major challenges
for ACS users is to improve the relevance of the search results for t... | Information Retrieval |
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