id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.04998 | HourVideo: 1-Hour Video-Language Understanding | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We present HourVideo, a benchmark dataset for hour-long video-language understanding. Our dataset consists of a novel task suite comprising summarization, perception (recall, tracking), visual reasoning (spatial, temporal, predictive, causal, counterfactual), and navigation (room-to-room, object retrieval) tasks. HourV... | {
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2411.04999 | DynaMem: Online Dynamic Spatio-Semantic Memory for Open World Mobile
Manipulation | [
"cs.RO",
"cs.LG"
] | Significant progress has been made in open-vocabulary mobile manipulation, where the goal is for a robot to perform tasks in any environment given a natural language description. However, most current systems assume a static environment, which limits the system's applicability in real-world scenarios where environments... | {
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2411.05000 | Needle Threading: Can LLMs Follow Threads through Near-Million-Scale
Haystacks? | [
"cs.CL"
] | As the context limits of Large Language Models (LLMs) increase, the range of possible applications and downstream functions broadens. In many real-world tasks, decisions depend on details scattered across collections of often disparate documents containing mostly irrelevant information. Long-context LLMs appear well-su... | {
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2411.05001 | Analyzing The Language of Visual Tokens | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | With the introduction of transformer-based models for vision and language tasks, such as LLaVA and Chameleon, there has been renewed interest in the discrete tokenized representation of images. These models often treat image patches as discrete tokens, analogous to words in natural language, learning joint alignments b... | {
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2411.05003 | ReCapture: Generative Video Camera Controls for User-Provided Videos
using Masked Video Fine-Tuning | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | Recently, breakthroughs in video modeling have allowed for controllable camera trajectories in generated videos. However, these methods cannot be directly applied to user-provided videos that are not generated by a video model. In this paper, we present ReCapture, a method for generating new videos with novel camera tr... | {
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2411.05005 | Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion
Models | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most existing work treats diffusion models as a standalone component for perception tasks, employing them either solely for off-the-shelf data augmentation or as mere feature extra... | {
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2411.05006 | ProEdit: Simple Progression is All You Need for High-Quality 3D Scene
Editing | [
"cs.CV"
] | This paper proposes ProEdit - a simple yet effective framework for high-quality 3D scene editing guided by diffusion distillation in a novel progressive manner. Inspired by the crucial observation that multi-view inconsistency in scene editing is rooted in the diffusion model's large feasible output space (FOS), our fr... | {
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2411.05007 | SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion
Models | [
"cs.CV",
"cs.LG"
] | Diffusion models have been proven highly effective at generating high-quality images. However, as these models grow larger, they require significantly more memory and suffer from higher latency, posing substantial challenges for deployment. In this work, we aim to accelerate diffusion models by quantizing their weights... | {
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2411.05010 | Scattered Forest Search: Smarter Code Space Exploration with LLMs | [
"cs.SE",
"cs.AI",
"cs.LG"
] | We propose a novel approach to scaling LLM inference for code generation. We frame code generation as a black box optimization problem within the code space, and employ optimization-inspired techniques to enhance exploration. Specifically, we introduce Scattered Forest Search to enhance solution diversity while searchi... | {
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2411.05011 | De la Extensi\'on a la Investigaci\'on: Como La Rob\'otica Estimula el
Inter\'es Acad\'emico en Estudiantes de Grado | [
"cs.CY",
"cs.RO"
] | This research examines the impact of robotics groups in higher education, focusing on how these activities influence the development of transversal skills and academic motivation. While robotics goes beyond just technical knowledge, participation in these groups has been observed to significantly improve skills such as... | {
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2411.05013 | Enhancing literature review with LLM and NLP methods. Algorithmic
trading case | [
"q-fin.ST",
"cs.AI",
"cs.LG",
"q-fin.TR"
] | This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles published between 1956 and Q1 2020. We compare traditional practices-such as keyword-based algorithms and e... | {
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2411.05014 | Fast and interpretable electricity consumption scenario generation for
individual consumers | [
"stat.AP",
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
] | To enable the transition from fossil fuels towards renewable energy, the low-voltage grid needs to be reinforced at a faster pace and on a larger scale than was historically the case. To efficiently plan reinforcements, one needs to estimate the currents and voltages throughout the grid, which are unknown but can be ca... | {
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2411.05016 | Reservoir computing for system identification and predictive control
with limited data | [
"eess.SY",
"cs.LG",
"cs.SY",
"math.DS"
] | Model predictive control (MPC) is an industry standard control technique that iteratively solves an open-loop optimization problem to guide a system towards a desired state or trajectory. Consequently, an accurate forward model of system dynamics is critical for the efficacy of MPC and much recent work has been aimed a... | {
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2411.05022 | Towards Probabilistic Planning of Explanations for Robot Navigation | [
"cs.AI",
"cs.RO"
] | In robotics, ensuring that autonomous systems are comprehensible and accountable to users is essential for effective human-robot interaction. This paper introduces a novel approach that integrates user-centered design principles directly into the core of robot path planning processes. We propose a probabilistic framewo... | {
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2411.05023 | Multimodal Quantum Natural Language Processing: A Novel Framework for
using Quantum Methods to Analyse Real Data | [
"cs.CL",
"cs.LG",
"quant-ph"
] | Despite significant advances in quantum computing across various domains, research on applying quantum approaches to language compositionality - such as modeling linguistic structures and interactions - remains limited. This gap extends to the integration of quantum language data with real-world data from sources like ... | {
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2411.05025 | LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and
Perceptions | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.DL",
"cs.HC"
] | The rise of large language models (LLMs) has led many researchers to consider their usage for scientific work. Some have found benefits using LLMs to augment or automate aspects of their research pipeline, while others have urged caution due to risks and ethical concerns. Yet little work has sought to quantify and char... | {
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2411.05026 | Deep Learning and Machine Learning -- Natural Language Processing: From
Theory to Application | [
"cs.CL",
"cs.HC"
] | With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intelligence. As artificial intelligence continues to revolutionize fields from healthcare to finance, NLP techniques such as tokenization, text ... | {
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2411.05027 | Generative Artificial Intelligence Meets Synthetic Aperture Radar: A
Survey | [
"cs.CV",
"cs.AI",
"eess.IV"
] | SAR images possess unique attributes that present challenges for both human observers and vision AI models to interpret, owing to their electromagnetic characteristics. The interpretation of SAR images encounters various hurdles, with one of the primary obstacles being the data itself, which includes issues related to ... | {
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2411.05028 | Leveraging Transfer Learning and Multiple Instance Learning for HER2
Automatic Scoring of H\&E Whole Slide Images | [
"cs.CV",
"cs.AI",
"q-bio.QM"
] | Expression of human epidermal growth factor receptor 2 (HER2) is an important biomarker in breast cancer patients who can benefit from cost-effective automatic Hematoxylin and Eosin (H\&E) HER2 scoring. However, developing such scoring models requires large pixel-level annotated datasets. Transfer learning allows prior... | {
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2411.05029 | Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of
Public and Private Lung Ultrasound Datasets and Studies | [
"cs.CV",
"cs.AI"
] | The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging mod... | {
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2411.05030 | EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy
to Smart Microfluidic Single-Cells Analysis | [
"q-bio.QM",
"cs.CV",
"eess.IV"
] | Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data over extended periods is challenging, particularly in achieving accurate and consistent real-time event classification at the intersection ... | {
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2411.05031 | On-Device Emoji Classifier Trained with GPT-based Data Augmentation for
a Mobile Keyboard | [
"cs.CL"
] | Emojis improve communication quality among smart-phone users that use mobile keyboards to exchange text. To predict emojis for users based on input text, we should consider the on-device low memory and time constraints, ensure that the on-device emoji classifier covers a wide range of emoji classes even though the emoj... | {
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2411.05033 | Diagonalization without Diagonalization: A Direct Optimization Approach
for Solid-State Density Functional Theory | [
"physics.chem-ph",
"cs.LG",
"physics.comp-ph"
] | We present a novel approach to address the challenges of variable occupation numbers in direct optimization of density functional theory (DFT). By parameterizing both the eigenfunctions and the occupation matrix, our method minimizes the free energy with respect to these parameters. As the stationary conditions require... | {
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2411.05034 | Mitigating Privacy Risks in LLM Embeddings from Embedding Inversion | [
"cs.CR",
"cs.AI",
"cs.CL"
] | Embeddings have become a cornerstone in the functionality of large language models (LLMs) due to their ability to transform text data into rich, dense numerical representations that capture semantic and syntactic properties. These embedding vector databases serve as the long-term memory of LLMs, enabling efficient hand... | {
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2411.05036 | From Word Vectors to Multimodal Embeddings: Techniques, Applications,
and Future Directions For Large Language Models | [
"cs.CL"
] | Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces. This review visits foundational concepts such as the distributional hypothesis and contextual similarity, tracing the evolution from sparse repres... | {
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2411.05037 | Towards Interpreting Language Models: A Case Study in Multi-Hop
Reasoning | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Answering multi-hop reasoning questions requires retrieving and synthesizing information from diverse sources. Language models (LMs) struggle to perform such reasoning consistently. We propose an approach to pinpoint and rectify multi-hop reasoning failures through targeted memory injections on LM attention heads. Firs... | {
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2411.05039 | YouTube Comments Decoded: Leveraging LLMs for Low Resource Language
Classification | [
"cs.CL",
"cs.AI"
] | Sarcasm detection is a significant challenge in sentiment analysis, particularly due to its nature of conveying opinions where the intended meaning deviates from the literal expression. This challenge is heightened in social media contexts where code-mixing, especially in Dravidian languages, is prevalent. Code-mixing ... | {
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2411.05040 | Bottom-Up and Top-Down Analysis of Values, Agendas, and Observations in
Corpora and LLMs | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) generate diverse, situated, persuasive texts from a plurality of potential perspectives, influenced heavily by their prompts and training data. As part of LLM adoption, we seek to characterize - and ideally, manage - the socio-cultural values that they express, for reasons of safety, accura... | {
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2411.05042 | Improving Radiology Report Conciseness and Structure via Local Large
Language Models | [
"cs.CL",
"cs.AI"
] | In this study, we aim to enhance radiology reporting by improving both the conciseness and structured organization of findings (also referred to as templating), specifically by organizing information according to anatomical regions. This structured approach allows physicians to locate relevant information quickly, incr... | {
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2411.05043 | Multi-language Video Subtitle Dataset for Image-based Text Recognition | [
"cs.CV",
"cs.AI"
] | The Multi-language Video Subtitle Dataset is a comprehensive collection designed to support research in text recognition across multiple languages. This dataset includes 4,224 subtitle images extracted from 24 videos sourced from online platforms. It features a wide variety of characters, including Thai consonants, vow... | {
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2411.05044 | Deep Heuristic Learning for Real-Time Urban Pathfinding | [
"cs.AI",
"cs.LG",
"stat.ML"
] | This paper introduces a novel approach to urban pathfinding by transforming traditional heuristic-based algorithms into deep learning models that leverage real-time contextual data, such as traffic and weather conditions. We propose two methods: an enhanced A* algorithm that dynamically adjusts routes based on current ... | {
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2411.05045 | Performance-Guided LLM Knowledge Distillation for Efficient Text
Classification at Scale | [
"cs.CL"
] | Large Language Models (LLMs) face significant challenges at inference time due to their high computational demands. To address this, we present Performance-Guided Knowledge Distillation (PGKD), a cost-effective and high-throughput solution for production text classification applications. PGKD utilizes teacher-student K... | {
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2411.05046 | PhoneLM:an Efficient and Capable Small Language Model Family through
Principled Pre-training | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The interest in developing small language models (SLM) for on-device deployment is fast growing. However, the existing SLM design hardly considers the device hardware characteristics. Instead, this work presents a simple yet effective principle for SLM design: architecture searching for (near-)optimal runtime efficienc... | {
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2411.05048 | Leveraging LLMs to Enable Natural Language Search on Go-to-market
Platforms | [
"cs.CL",
"cs.AI",
"cs.DB",
"cs.IR",
"cs.LG"
] | Enterprise searches require users to have complex knowledge of queries, configurations, and metadata, rendering it difficult for them to access information as needed. Most go-to-market (GTM) platforms utilize advanced search, an interface that enables users to filter queries by various fields using categories or keywor... | {
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2411.05049 | ProverbEval: Exploring LLM Evaluation Challenges for Low-resource
Language Understanding | [
"cs.CL"
] | With the rapid development of evaluation datasets to assess LLMs understanding across a wide range of subjects and domains, identifying a suitable language understanding benchmark has become increasingly challenging. In this work, we explore LLM evaluation challenges for low-resource language understanding and introduc... | {
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2411.05050 | Selecting Between BERT and GPT for Text Classification in Political
Science Research | [
"cs.CL",
"cs.AI"
] | Political scientists often grapple with data scarcity in text classification. Recently, fine-tuned BERT models and their variants have gained traction as effective solutions to address this issue. In this study, we investigate the potential of GPT-based models combined with prompt engineering as a viable alternative. W... | {
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2411.05051 | Intellectual Property Protection for Deep Learning Model and Dataset
Intelligence | [
"cs.CR",
"cs.AI",
"cs.LG"
] | With the growing applications of Deep Learning (DL), especially recent spectacular achievements of Large Language Models (LLMs) such as ChatGPT and LLaMA, the commercial significance of these remarkable models has soared. However, acquiring well-trained models is costly and resource-intensive. It requires a considerabl... | {
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2411.05052 | The Fibonacci Network: A Simple Alternative for Positional Encoding | [
"cs.LG"
] | Coordinate-based Multi-Layer Perceptrons (MLPs) are known to have difficulty reconstructing high frequencies of the training data. A common solution to this problem is Positional Encoding (PE), which has become quite popular. However, PE has drawbacks. It has high-frequency artifacts and adds another hyper-hyperparamet... | {
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2411.05054 | FMEA Builder: Expert Guided Text Generation for Equipment Maintenance | [
"cs.CL",
"cs.AI"
] | Foundation models show great promise for generative tasks in many domains. Here we discuss the use of foundation models to generate structured documents related to critical assets. A Failure Mode and Effects Analysis (FMEA) captures the composition of an asset or piece of equipment, the ways it may fail and the consequ... | {
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2411.05055 | Integrating Large Language Models for Genetic Variant Classification | [
"q-bio.GN",
"cs.AI",
"cs.LG"
] | The classification of genetic variants, particularly Variants of Uncertain Significance (VUS), poses a significant challenge in clinical genetics and precision medicine. Large Language Models (LLMs) have emerged as transformative tools in this realm. These models can uncover intricate patterns and predictive insights t... | {
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2411.05056 | Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal
Language Models | [
"cs.CR",
"cs.AI"
] | Multi-Modal Language Models (MLLMs) have transformed artificial intelligence by combining visual and text data, making applications like image captioning, visual question answering, and multi-modal content creation possible. This ability to understand and work with complex information has made MLLMs useful in areas suc... | {
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2411.05057 | A Brief History of Named Entity Recognition | [
"cs.CL"
] | A large amount of information in today's world is now stored in knowledge bases. Named Entity Recognition (NER) is a process of extracting, disambiguation, and linking an entity from raw text to insightful and structured knowledge bases. More concretely, it is identifying and classifying entities in the text that are c... | {
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2411.05059 | FineTuneBench: How well do commercial fine-tuning APIs infuse knowledge
into LLMs? | [
"cs.CL",
"cs.AI",
"cs.IR"
] | There is great interest in fine-tuning frontier large language models (LLMs) to inject new information and update existing knowledge. While commercial LLM fine-tuning APIs from providers such as OpenAI and Google promise flexible adaptation for various applications, the efficacy of fine-tuning remains unclear. In this ... | {
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2411.05060 | A Guide to Misinformation Detection Datasets | [
"cs.SI",
"cs.CL",
"cs.CY"
] | Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this problem, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of all of the 36 datasets that consist ... | {
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2411.05079 | Precision or Recall? An Analysis of Image Captions for Training
Text-to-Image Generation Model | [
"cs.CV",
"cs.CL"
] | Despite advancements in text-to-image models, generating images that precisely align with textual descriptions remains challenging due to misalignment in training data. In this paper, we analyze the critical role of caption precision and recall in text-to-image model training. Our analysis of human-annotated captions s... | {
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2411.05085 | PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology
Report Generation | [
"cs.AI",
"cs.CL",
"cs.CV"
] | Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Grounded radiology report generation (GRRG) extends RRG by including the localisation of individual findings on the image. Currently, there are no manually annotated chest X-ray (CXR) datasets to train GRRG models. In th... | {
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2411.05088 | Findings of the IWSLT 2024 Evaluation Campaign | [
"cs.CL"
] | This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, dialect and low-resource speech translation, and Indic lan... | {
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2411.05091 | Watermarking Language Models through Language Models | [
"cs.LG",
"cs.CL",
"cs.CR"
] | This paper presents a novel framework for watermarking language models through prompts generated by language models. The proposed approach utilizes a multi-model setup, incorporating a Prompting language model to generate watermarking instructions, a Marking language model to embed watermarks within generated content, ... | {
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2411.05097 | On the cohesion and separability of average-link for hierarchical
agglomerative clustering | [
"cs.LG",
"cs.DS"
] | Average-link is widely recognized as one of the most popular and effective methods for building hierarchical agglomerative clustering. The available theoretical analyses show that this method has a much better approximation than other popular heuristics, as single-linkage and complete-linkage, regarding variants of Das... | {
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2411.05102 | EnchantedClothes: Visual and Tactile Feedback with an Abdomen-Attached
Robot through Clothes | [
"cs.RO",
"cs.HC"
] | Wearable robots are designed to be worn on the human body. Taking advantage of their physical form, various applications for wearable robots are being considered. This study proposes a wearable robot worn on the abdomen and a new interaction with it. Our robot enables a variety of applications related to communication ... | {
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2411.05107 | MissionGPT: Mission Planner for Mobile Robot based on Robotics
Transformer Model | [
"cs.RO"
] | This paper presents a novel approach to building mission planners based on neural networks with Transformer architecture and Large Language Models (LLMs). This approach demonstrates the possibility of setting a task for a mobile robot and its successful execution without the use of perception algorithms, based only on ... | {
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2411.05111 | Location-Based Output Adaptation for Enhanced Actuator Performance using
Frequency Sweep Analysis | [
"cs.HC",
"cs.RO"
] | This paper presents a methodology for enhancing actuator performance in older devices or retrofitting devices with haptic feedback actuators. The approach is versatile, accommodating various actuator and mounting positions. Through a frequency sweep analysis, the system's characteristics are captured, enabling the crea... | {
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2411.05118 | An emotional expression system with vibrotactile feedback during the
robot's speech | [
"cs.HC",
"cs.RO"
] | This study aimed to develop a system that provides vibrotactile feedback corresponding to the emotional content of text when a communication robot speaks. We used OpenAI's "GPT-4o Mini" for emotion estimation, extracting valence and arousal values from the text. The amplitude and frequency of vibrotactile stimulation u... | {
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2411.05119 | Exploiting the Structure of Two Graphs with Graph Neural Networks | [
"cs.LG",
"eess.SP"
] | Graph neural networks (GNNs) have emerged as a promising solution to deal with unstructured data, outperforming traditional deep learning architectures. However, most of the current GNN models are designed to work with a single graph, which limits their applicability in many real-world scenarios where multiple graphs m... | {
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2411.05122 | Socially Assistive Robots: A Technological Approach to Emotional Support | [
"cs.RO",
"cs.HC"
] | In today's high-pressure and isolated society, the demand for emotional support has surged, necessitating innovative solutions. Socially Assistive Robots (SARs) offer a technological approach to providing emotional assistance by leveraging advanced robotics, artificial intelligence, and sensor technologies. This study ... | {
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2411.05129 | Silicone-made Tactile Actuator Integrated with Hot Thermo-fiber Finger
Sleeve | [
"cs.HC",
"cs.RO"
] | Multi-mode haptic feedback is essential to achieve high realism and immersion in virtual environments. This paper proposed a novel silicone fingertip actuator integrated with a hot thermal fabric finger sleeve to render pressure, vibration, and hot thermal feedback simultaneously. The actuator is pneumatically actuated... | {
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2411.05137 | Inclusion in Assistive Haircare Robotics: Practical and Ethical
Considerations in Hair Manipulation | [
"cs.RO"
] | Robot haircare systems could provide a controlled and personalized environment that is respectful of an individual's sensitivities and may offer a comfortable experience. We argue that because of hair and hairstyles' often unique importance in defining and expressing an individual's identity, we should approach the dev... | {
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2411.05138 | Vibrotactile Feedback for a Remote Operated Robot with Noise Subtraction
Based on Perceived Intensity | [
"cs.RO",
"cs.HC"
] | There is a growing demand for teleoperated robots. This paper presents a novel method for reducing vibration noise generated by robot's own motion, which can disrupt the quality of tactile feedback for teleoperated robots. Our approach focuses on perceived intensity, the amount of how humans experience vibration, to cr... | {
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2411.05164 | Conveying Surroundings Information of a Robot End-Effector by Adjusting
Controller Button Stiffness | [
"cs.RO",
"cs.HC"
] | This study addresses the challenge of low dexterity in teleoperation tasks caused by limited sensory feedback and visual occlusion. We propose a novel approach that integrates haptic feedback into teleoperation using the adaptive triggers of a commercially available DualSense controller. By adjusting button stiffness b... | {
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2411.05167 | EPIC: Enhancing Privacy through Iterative Collaboration | [
"cs.LG",
"cs.CR"
] | Advancements in genomics technology lead to a rising volume of viral (e.g., SARS-CoV-2) sequence data, resulting in increased usage of machine learning (ML) in bioinformatics. Traditional ML techniques require centralized data collection and processing, posing challenges in realistic healthcare scenarios. Additionally,... | {
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2411.05172 | ImpScore: A Learnable Metric For Quantifying The Implicitness Level of
Language | [
"cs.CL"
] | Handling implicit language is essential for natural language processing systems to achieve precise text understanding and facilitate natural interactions with users. Despite its importance, the absence of a metric for accurately measuring the implicitness of language significantly constrains the depth of analysis possi... | {
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2411.05173 | DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging | [
"cs.LG"
] | Federated Learning (FL) is a distributed learning technique that maintains data privacy by providing a decentralized training method for machine learning models using distributed big data. This promising Federated Learning approach has also gained popularity in bioinformatics, where the privacy of biomedical data holds... | {
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2411.05174 | Inverse Transition Learning: Learning Dynamics from Demonstrations | [
"cs.LG",
"cs.AI",
"stat.ML"
] | We consider the problem of estimating the transition dynamics $T^*$ from near-optimal expert trajectories in the context of offline model-based reinforcement learning. We develop a novel constraint-based method, Inverse Transition Learning, that treats the limited coverage of the expert trajectories as a \emph{feature}... | {
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2411.05183 | Interpretable Measurement of CNN Deep Feature Density using Copula and
the Generalized Characteristic Function | [
"cs.CV",
"cs.LG"
] | We present a novel empirical approach toward measuring the Probability Density Function (PDF) of the deep features of Convolutional Neural Networks (CNNs). Measurement of the deep feature PDF is a valuable problem for several reasons. Notably, a. Understanding the deep feature PDF yields new insight into deep represent... | {
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2411.05184 | Discern-XR: An Online Classifier for Metaverse Network Traffic | [
"cs.AI",
"eess.SP"
] | In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality of Metaverse services. Leveraging segmented learning, the Frame Vector Representation (FVR) algorithm and Frame Identification Algorithm (FI... | {
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2411.05188 | AGE2HIE: Transfer Learning from Brain Age to Predicting Neurocognitive
Outcome for Infant Brain Injury | [
"eess.IV",
"cs.CV",
"cs.LG",
"q-bio.NC"
] | Hypoxic-Ischemic Encephalopathy (HIE) affects 1 to 5 out of every 1,000 newborns, with 30% to 50% of cases resulting in adverse neurocognitive outcomes. However, these outcomes can only be reliably assessed as early as age 2. Therefore, early and accurate prediction of HIE-related neurocognitive outcomes using deep lea... | {
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2411.05189 | Adversarial Robustness of In-Context Learning in Transformers for Linear
Regression | [
"cs.LG",
"cs.CR"
] | Transformers have demonstrated remarkable in-context learning capabilities across various domains, including statistical learning tasks. While previous work has shown that transformers can implement common learning algorithms, the adversarial robustness of these learned algorithms remains unexplored. This work investig... | {
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2411.05192 | Explaining Mixtures of Sources in News Articles | [
"cs.CL",
"cs.AI"
] | Human writers plan, then write. For large language models (LLMs) to play a role in longer-form article generation, we must understand the planning steps humans make before writing. We explore one kind of planning, source-selection in news, as a case-study for evaluating plans in long-form generation. We ask: why do spe... | {
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2411.05193 | Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Value-based reinforcement learning (RL) can in principle learn effective policies for a wide range of multi-turn problems, from games to dialogue to robotic control, including via offline RL from static previously collected datasets. However, despite the widespread use of policy gradient methods to train large language... | {
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2411.05194 | Interactive Dialogue Agents via Reinforcement Learning on Hindsight
Regenerations | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Recent progress on large language models (LLMs) has enabled dialogue agents to generate highly naturalistic and plausible text. However, current LLM language generation focuses on responding accurately to questions and requests with a single effective response. In reality, many real dialogues are interactive, meaning a... | {
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2411.05195 | Exploring How Generative MLLMs Perceive More Than CLIP with the Same
Vision Encoder | [
"cs.LG",
"cs.CL",
"cs.CV"
] | Recent research has shown that CLIP models struggle with visual reasoning tasks that require grounding compositionality, understanding spatial relationships, or capturing fine-grained details. One natural hypothesis is that the CLIP vision encoder does not embed essential information for these tasks. However, we find t... | {
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2411.05196 | Explainable AI through a Democratic Lens: DhondtXAI for Proportional
Feature Importance Using the D'Hondt Method | [
"cs.AI",
"cs.DL",
"cs.LG"
] | In democratic societies, electoral systems play a crucial role in translating public preferences into political representation. Among these, the D'Hondt method is widely used to ensure proportional representation, balancing fair representation with governmental stability. Recently, there has been a growing interest in ... | {
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2411.05197 | Hardware and Software Platform Inference | [
"cs.LG"
] | It is now a common business practice to buy access to large language model (LLM) inference rather than self-host, because of significant upfront hardware infrastructure and energy costs. However, as a buyer, there is no mechanism to verify the authenticity of the advertised service including the serving hardware platfo... | {
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2411.05198 | Private Algorithms for Stochastic Saddle Points and Variational
Inequalities: Beyond Euclidean Geometry | [
"cs.LG",
"cs.CR",
"math.OC",
"stat.ML"
] | In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of $(\epsilon,\delta)$-differential privacy (DP) in both Euclidean and non-Euclidean setups. We first consider Lipschitz convex-concave SSPs in the $\ell_p/\ell_q$ set... | {
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2411.05199 | CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement | [
"cs.CL"
] | Large Language Models (LLMs) have revolutionized code generation but require significant resources and often over-generalize, limiting their task-specific efficiency. Fine-tuning smaller, open-source LLMs provides a cost-effective alternative. However, standard supervised approaches rely only on correct examples, missi... | {
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2411.05200 | Toward Cultural Interpretability: A Linguistic Anthropological Framework
for Describing and Evaluating Large Language Models (LLMs) | [
"cs.CY",
"cs.CL",
"cs.HC",
"cs.LG"
] | This article proposes a new integration of linguistic anthropology and machine learning (ML) around convergent interests in both the underpinnings of language and making language technologies more socially responsible. While linguistic anthropology focuses on interpreting the cultural basis for human language use, the ... | {
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2411.05205 | Maximizing User Connectivity in AI-Enabled Multi-UAV Networks: A
Distributed Strategy Generalized to Arbitrary User Distributions | [
"eess.SY",
"cs.AI",
"cs.NI",
"cs.SY"
] | Deep reinforcement learning (DRL) has been extensively applied to Multi-Unmanned Aerial Vehicle (UAV) network (MUN) to effectively enable real-time adaptation to complex, time-varying environments. Nevertheless, most of the existing works assume a stationary user distribution (UD) or a dynamic one with predicted patter... | {
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2411.05209 | Alopex: A Computational Framework for Enabling On-Device Function Calls
with LLMs | [
"cs.AI",
"cs.CL"
] | The rapid advancement of Large Language Models (LLMs) has led to their increased integration into mobile devices for personalized assistance, which enables LLMs to call external API functions to enhance their performance. However, challenges such as data scarcity, ineffective question formatting, and catastrophic forge... | {
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2411.05212 | RT-Grasp: Reasoning Tuning Robotic Grasping via Multi-modal Large
Language Model | [
"cs.RO"
] | Recent advances in Large Language Models (LLMs) have showcased their remarkable reasoning capabilities, making them influential across various fields. However, in robotics, their use has primarily been limited to manipulation planning tasks due to their inherent textual output. This paper addresses this limitation by i... | {
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2411.05214 | STAND-Guard: A Small Task-Adaptive Content Moderation Model | [
"cs.CL"
] | Content moderation, the process of reviewing and monitoring the safety of generated content, is important for development of welcoming online platforms and responsible large language models. Content moderation contains various tasks, each with its unique requirements tailored to specific scenarios. Therefore, it is cru... | {
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2411.05219 | Anticipatory Understanding of Resilient Agriculture to Climate | [
"cs.CV",
"cs.LG",
"stat.AP"
] | With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, d... | {
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2411.05222 | Don't Look Twice: Faster Video Transformers with Run-Length Tokenization | [
"cs.CV",
"cs.LG"
] | Transformers are slow to train on videos due to extremely large numbers of input tokens, even though many video tokens are repeated over time. Existing methods to remove such uninformative tokens either have significant overhead, negating any speedup, or require tuning for different datasets and examples. We present Ru... | {
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2411.05223 | Generalizable Single-Source Cross-modality Medical Image Segmentation
via Invariant Causal Mechanisms | [
"cs.CV",
"cs.LG"
] | Single-source domain generalization (SDG) aims to learn a model from a single source domain that can generalize well on unseen target domains. This is an important task in computer vision, particularly relevant to medical imaging where domain shifts are common. In this work, we consider a challenging yet practical sett... | {
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2411.05224 | Beyond the Numbers: Transparency in Relation Extraction Benchmark
Creation and Leaderboards | [
"cs.CL"
] | This paper investigates the transparency in the creation of benchmarks and the use of leaderboards for measuring progress in NLP, with a focus on the relation extraction (RE) task. Existing RE benchmarks often suffer from insufficient documentation, lacking crucial details such as data sources, inter-annotator agreemen... | {
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2411.05225 | Breaking The Ice: Video Segmentation for Close-Range Ice-Covered Waters | [
"cs.CV"
] | Rapid ice recession in the Arctic Ocean, with predictions of ice-free summers by 2060, opens new maritime routes but requires reliable navigation solutions. Current approaches rely heavily on subjective expert judgment, underscoring the need for automated, data-driven solutions. This study leverages machine learning to... | {
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2411.05227 | CHATTER: A Character Attribution Dataset for Narrative Understanding | [
"cs.CL"
] | Computational narrative understanding studies the identification, description, and interaction of the elements of a narrative: characters, attributes, events, and relations. Narrative research has given considerable attention to defining and classifying character types. However, these character-type taxonomies do not g... | {
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2411.05228 | Solving Hidden Monotone Variational Inequalities with Surrogate Losses | [
"cs.LG",
"math.OC"
] | Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minimizing projected Bellman error and min-max optimization, cannot be modelled as minimizing a scalar loss function but instead correspond to solving a variational inequality (VI) prob... | {
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2411.05231 | Evaluating GPT-4 at Grading Handwritten Solutions in Math Exams | [
"cs.CY",
"cs.CL",
"cs.LG"
] | Recent advances in generative artificial intelligence (AI) have shown promise in accurately grading open-ended student responses. However, few prior works have explored grading handwritten responses due to a lack of data and the challenge of combining visual and textual information. In this work, we leverage state-of-t... | {
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} |
2411.05232 | Abstract2Appendix: Academic Reviews Enhance LLM Long-Context
Capabilities | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have shown remarkable performance across various tasks, yet their ability to handle long-context reading remains challenging. This study explores the effectiveness of leveraging high-quality academic peer review data for fine-tuning LLMs to enhance their long-context capabilities. We compar... | {
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2411.05234 | Performative Reinforcement Learning with Linear Markov Decision Process | [
"cs.LG",
"cs.GT"
] | We study the setting of \emph{performative reinforcement learning} where the deployed policy affects both the reward, and the transition of the underlying Markov decision process. Prior work~\parencite{MTR23} has addressed this problem under the tabular setting and established last-iterate convergence of repeated retra... | {
"Other": 1,
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"cs.SY": 0
} |
2411.05236 | Designing a Light-based Communication System with a Biomolecular
Receiver | [
"cs.IT",
"math.IT"
] | Biological systems transduce signals from their surroundings in numerous ways. This paper introduces a communication system using the light-gated ion channel Channelrhodopsin-2 (ChR2), which causes an ion current to flow in response to light. Our design includes a ChR2-based receiver along with encoding, modulation tec... | {
"Other": 0,
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} |
2411.05237 | Pruning the Path to Optimal Care: Identifying Systematically Suboptimal
Medical Decision-Making with Inverse Reinforcement Learning | [
"cs.LG",
"q-bio.QM",
"stat.AP",
"stat.CO",
"stat.ML"
] | In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning (IRL) that identifies suboptimal clinician actions based on the actions of their peers. This approach centers two stages of IRL with an int... | {
"Other": 0,
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} |
2411.05238 | Generating Highly Designable Proteins with Geometric Algebra Flow
Matching | [
"cs.LG",
"stat.ML"
] | We introduce a generative model for protein backbone design utilizing geometric products and higher order message passing. In particular, we propose Clifford Frame Attention (CFA), an extension of the invariant point attention (IPA) architecture from AlphaFold2, in which the backbone residue frames and geometric featur... | {
"Other": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.05239 | ZipNN: Lossless Compression for AI Models | [
"cs.LG",
"cs.IT",
"math.IT"
] | With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literature deleting parts of the model weights for faster inference, we investigate a more traditional type ... | {
"Other": 0,
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"cs.MA": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.05243 | An Integrated Epidemic Simulation Workflow for Submodular Intervention
Strategies | [
"cs.SI"
] | Owing to the ongoing COVID-19 pandemic and other recent global epidemics, epidemic simulation frameworks are gaining rapid significance. In this work, we present a workflow that will allow researchers to simulate the spread of an infectious disease under different intervention schemes. Our workflow is built using the C... | {
"Other": 0,
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 1,
"cs.SY": 0
} |
2411.05253 | What talking you?: Translating Code-Mixed Messaging Texts to English | [
"cs.CL"
] | Translation of code-mixed texts to formal English allow a wider audience to understand these code-mixed languages, and facilitate downstream analysis applications such as sentiment analysis. In this work, we look at translating Singlish, which is colloquial Singaporean English, to formal standard English. Singlish is f... | {
"Other": 0,
"cs.AI": 0,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.05254 | Hierarchical Visual Feature Aggregation for OCR-Free Document
Understanding | [
"cs.CV"
] | We present a novel OCR-free document understanding framework based on pretrained Multimodal Large Language Models (MLLMs). Our approach employs multi-scale visual features to effectively handle various font sizes within document images. To address the increasing costs of considering the multi-scale visual inputs for ML... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2411.05260 | QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure
Federated Learning | [
"cs.CR",
"cs.AI",
"cs.DC"
] | Federated Learning has emerged as a leading approach for decentralized machine learning, enabling multiple clients to collaboratively train a shared model without exchanging private data. While FL enhances data privacy, it remains vulnerable to inference attacks, such as gradient inversion and membership inference, dur... | {
"Other": 1,
"cs.AI": 1,
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"cs.CR": 1,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.05261 | Decoding Report Generators: A Cyclic Vision-Language Adapter for
Counterfactual Explanations | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | Despite significant advancements in report generation methods, a critical limitation remains: the lack of interpretability in the generated text. This paper introduces an innovative approach to enhance the explainability of text generated by report generation models. Our method employs cyclic text manipulation and visu... | {
"Other": 0,
"cs.AI": 1,
"cs.CE": 0,
"cs.CL": 1,
"cs.CR": 0,
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"cs.MA": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.05263 | Minimal Conditions for Beneficial Neighbourhood Search and Local Descent | [
"cs.AI"
] | This paper investigates what properties a neighbourhood requires to support beneficial local search. We show that neighbourhood locality, and a reduction in cost probability towards the optimum, support a proof that search among neighbours is more likely to find an improving solution in a single search step than blind ... | {
"Other": 0,
"cs.AI": 1,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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