id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.05465 | Small Language Models (SLMs) Can Still Pack a Punch: A survey | [
"cs.CL"
] | As foundation AI models continue to increase in size, an important question arises - is massive scale the only path forward? This survey of about 160 papers presents a family of Small Language Models (SLMs) in the 1 to 8 billion parameter range that demonstrate smaller models can perform as well, or even outperform lar... | {
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2501.05468 | LatteReview: A Multi-Agent Framework for Systematic Review Automation
Using Large Language Models | [
"cs.CL"
] | Systematic literature reviews and meta-analyses are essential for synthesizing research insights, but they remain time-intensive and labor-intensive due to the iterative processes of screening, evaluation, and data extraction. This paper introduces and evaluates LatteReview, a Python-based framework that leverages larg... | {
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2501.05470 | RTLSquad: Multi-Agent Based Interpretable RTL Design | [
"cs.AR",
"cs.AI",
"cs.SE"
] | Optimizing Register-Transfer Level (RTL) code is crucial for improving hardware PPA performance. Large Language Models (LLMs) offer new approaches for automatic RTL code generation and optimization. However, existing methods often lack decision interpretability (sufficient, understandable justification for decisions), ... | {
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2501.05471 | Found in Translation: semantic approaches for enhancing AI
interpretability in face verification | [
"cs.CV",
"cs.AI",
"cs.HC",
"cs.LG"
] | The increasing complexity of machine learning models in computer vision, particularly in face verification, requires the development of explainable artificial intelligence (XAI) to enhance interpretability and transparency. This study extends previous work by integrating semantic concepts derived from human cognitive p... | {
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2501.05472 | The 2nd Place Solution from the 3D Semantic Segmentation Track in the
2024 Waymo Open Dataset Challenge | [
"cs.CV",
"cs.LG",
"cs.RO"
] | 3D semantic segmentation is one of the most crucial tasks in driving perception. The ability of a learning-based model to accurately perceive dense 3D surroundings often ensures the safe operation of autonomous vehicles. However, existing LiDAR-based 3D semantic segmentation databases consist of sequentially acquired L... | {
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2501.05473 | Implicit Guidance and Explicit Representation of Semantic Information in
Points Cloud: A Survey | [
"cs.CV"
] | Point clouds, a prominent method of 3D representation, are extensively utilized across industries such as autonomous driving, surveying, electricity, architecture, and gaming, and have been rigorously investigated for their accuracy and resilience. The extraction of semantic information from scenes enhances both human ... | {
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2501.05474 | Modality-Invariant Bidirectional Temporal Representation Distillation
Network for Missing Multimodal Sentiment Analysis | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.SD",
"eess.AS"
] | Multimodal Sentiment Analysis (MSA) integrates diverse modalities(text, audio, and video) to comprehensively analyze and understand individuals' emotional states. However, the real-world prevalence of incomplete data poses significant challenges to MSA, mainly due to the randomness of modality missing. Moreover, the he... | {
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2501.05475 | Retrieval-Augmented Generation by Evidence Retroactivity in LLMs | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Retrieval-augmented generation has gained significant attention due to its ability to integrate relevant external knowledge, enhancing the accuracy and reliability of the LLMs' responses. Most of the existing methods apply a dynamic multiple retrieval-generating process, to address multi-hop complex questions by decomp... | {
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2501.05476 | IntegrityAI at GenAI Detection Task 2: Detecting Machine-Generated
Academic Essays in English and Arabic Using ELECTRA and Stylometry | [
"cs.CL",
"cs.AI"
] | Recent research has investigated the problem of detecting machine-generated essays for academic purposes. To address this challenge, this research utilizes pre-trained, transformer-based models fine-tuned on Arabic and English academic essays with stylometric features. Custom models based on ELECTRA for English and Ara... | {
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2501.05478 | Language and Planning in Robotic Navigation: A Multilingual Evaluation
of State-of-the-Art Models | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG",
"cs.RO"
] | Large Language Models (LLMs) such as GPT-4, trained on huge amount of datasets spanning multiple domains, exhibit significant reasoning, understanding, and planning capabilities across various tasks. This study presents the first-ever work in Arabic language integration within the Vision-and-Language Navigation (VLN) d... | {
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2501.05479 | Practical Design and Benchmarking of Generative AI Applications for
Surgical Billing and Coding | [
"cs.CL",
"cs.LG"
] | Background: Healthcare has many manual processes that can benefit from automation and augmentation with Generative Artificial Intelligence (AI), the medical billing and coding process. However, current foundational Large Language Models (LLMs) perform poorly when tasked with generating accurate International Classifica... | {
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2501.05480 | The \textit{Questio de aqua et terra}: A Computational Authorship
Verification Study | [
"cs.CL",
"cs.DL"
] | The Questio de aqua et terra is a cosmological treatise traditionally attributed to Dante Alighieri. However, the authenticity of this text is controversial, due to discrepancies with Dante's established works and to the absence of contemporary references. This study investigates the authenticity of the Questio via com... | {
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2501.05482 | HP-BERT: A framework for longitudinal study of Hinduphobia on social
media via LLMs | [
"cs.CL",
"cs.SI"
] | During the COVID-19 pandemic, community tensions intensified, fuelling Hinduphobic sentiments and discrimination against individuals of Hindu descent within India and worldwide. Large language models (LLMs) have become prominent in natural language processing (NLP) tasks and social media analysis, enabling longitudinal... | {
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2501.05483 | Human Grasp Generation for Rigid and Deformable Objects with Decomposed
VQ-VAE | [
"cs.RO",
"cs.GR"
] | Generating realistic human grasps is crucial yet challenging for object manipulation in computer graphics and robotics. Current methods often struggle to generate detailed and realistic grasps with full finger-object interaction, as they typically rely on encoding the entire hand and estimating both posture and positio... | {
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2501.05484 | Tuning-Free Long Video Generation via Global-Local Collaborative
Diffusion | [
"cs.CV"
] | Creating high-fidelity, coherent long videos is a sought-after aspiration. While recent video diffusion models have shown promising potential, they still grapple with spatiotemporal inconsistencies and high computational resource demands. We propose GLC-Diffusion, a tuning-free method for long video generation. It mode... | {
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2501.05485 | S2 Chunking: A Hybrid Framework for Document Segmentation Through
Integrated Spatial and Semantic Analysis | [
"cs.CL",
"cs.IR",
"cs.LG"
] | Document chunking is a critical task in natural language processing (NLP) that involves dividing a document into meaningful segments. Traditional methods often rely solely on semantic analysis, ignoring the spatial layout of elements, which is crucial for understanding relationships in complex documents. This paper int... | {
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2501.05486 | Towards an Ontology of Traceable Impact Management in the Food Supply
Chain | [
"physics.soc-ph",
"cs.AI"
] | The pursuit of quality improvements and accountability in the food supply chains, especially how they relate to food-related outcomes, such as hunger, has become increasingly vital, necessitating a comprehensive approach that encompasses product quality and its impact on various stakeholders and their communities. Such... | {
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2501.05487 | The Future of AI: Exploring the Potential of Large Concept Models | [
"cs.CL"
] | The field of Artificial Intelligence (AI) continues to drive transformative innovations, with significant progress in conversational interfaces, autonomous vehicles, and intelligent content creation. Since the launch of ChatGPT in late 2022, the rise of Generative AI has marked a pivotal era, with the term Large Langua... | {
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2501.05488 | EndoDINO: A Foundation Model for GI Endoscopy | [
"eess.IV",
"cs.CV"
] | In this work, we present EndoDINO, a foundation model for GI endoscopy tasks that achieves strong generalizability by pre-training on a well-curated image dataset sampled from the largest known GI endoscopy video dataset in the literature. Specifically, we pre-trained ViT models with 1B, 307M, and 86M parameters using ... | {
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2501.05490 | Interpretable deep learning illuminates multiple structures fluorescence
imaging: a path toward trustworthy artificial intelligence in microscopy | [
"q-bio.SC",
"cs.AI",
"eess.IV"
] | Live-cell imaging of multiple subcellular structures is essential for understanding subcellular dynamics. However, the conventional multi-color sequential fluorescence microscopy suffers from significant imaging delays and limited number of subcellular structure separate labeling, resulting in substantial limitations f... | {
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2501.05493 | Monotonic Learning in the PAC Framework: A New Perspective | [
"cs.LG"
] | Monotone learning refers to learning processes in which expected performance consistently improves as more training data is introduced. Non-monotone behavior of machine learning has been the topic of a series of recent works, with various proposals that ensure monotonicity by applying transformations or wrappers on lea... | {
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2501.05494 | Mathematical Modeling and Machine Learning for Predicting Shade-Seeking
Behavior in Cows Under Heat Stress | [
"cs.LG"
] | In this paper we develop a mathematical model combined with machine learning techniques to predict shade-seeking behavior in cows exposed to heat stress. The approach integrates advanced mathematical features, such as time-averaged thermal indices and accumulated heat stress metrics, obtained by mathematical analysis o... | {
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2501.05495 | LSEBMCL: A Latent Space Energy-Based Model for Continual Learning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Continual learning has become essential in many practical applications such as online news summaries and product classification. The primary challenge is known as catastrophic forgetting, a phenomenon where a model inadvertently discards previously learned knowledge when it is trained on new tasks. Existing solutions i... | {
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2501.05496 | FedSA: A Unified Representation Learning via Semantic Anchors for
Prototype-based Federated Learning | [
"cs.LG",
"cs.AI"
] | Prototype-based federated learning has emerged as a promising approach that shares lightweight prototypes to transfer knowledge among clients with data heterogeneity in a model-agnostic manner. However, existing methods often collect prototypes directly from local models, which inevitably introduce inconsistencies into... | {
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2501.05497 | Spatial Information Integration in Small Language Models for Document
Layout Generation and Classification | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Document layout understanding is a field of study that analyzes the spatial arrangement of information in a document hoping to understand its structure and layout. Models such as LayoutLM (and its subsequent iterations) can understand semi-structured documents with SotA results; however, the lack of open semi-structure... | {
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2501.05498 | Generative Flow Networks: Theory and Applications to Structure Learning | [
"cs.LG"
] | Without any assumptions about data generation, multiple causal models may explain our observations equally well. To avoid selecting a single arbitrary model that could result in unsafe decisions if it does not match reality, it is therefore essential to maintain a notion of epistemic uncertainty about our possible cand... | {
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2501.05499 | Generalization of Urban Wind Environment Using Fourier Neural Operator
Across Different Wind Directions and Cities | [
"cs.LG",
"cs.CE",
"physics.flu-dyn"
] | Simulation of urban wind environments is crucial for urban planning, pollution control, and renewable energy utilization. However, the computational requirements of high-fidelity computational fluid dynamics (CFD) methods make them impractical for real cities. To address these limitations, this study investigates the e... | {
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2501.05501 | Strategy Masking: A Method for Guardrails in Value-based Reinforcement
Learning Agents | [
"cs.AI",
"cs.LG",
"cs.MA"
] | The use of reward functions to structure AI learning and decision making is core to the current reinforcement learning paradigm; however, without careful design of reward functions, agents can learn to solve problems in ways that may be considered "undesirable" or "unethical." Without thorough understanding of the ince... | {
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2501.05502 | Shrink the longest: improving latent space isotropy with symplicial
geometry | [
"cs.LG"
] | Although transformer-based models have been dominating the field of deep learning, various studies of their embedding space have shown that they suffer from "representation degeneration problem": embeddings tend to be distributed in a narrow cone, making the latent space highly anisotropic. Increasing the isotropy has ... | {
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2501.05503 | The more polypersonal the better -- a short look on space geometry of
fine-tuned layers | [
"cs.CL",
"cs.LG"
] | The interpretation of deep learning models is a rapidly growing field, with particular interest in language models. There are various approaches to this task, including training simpler models to replicate neural network predictions and analyzing the latent space of the model. The latter method allows us to not only id... | {
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2501.05510 | OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video
Understanding? | [
"cs.CV",
"cs.AI"
] | Temporal Awareness, the ability to reason dynamically based on the timestamp when a question is raised, is the key distinction between offline and online video LLMs. Unlike offline models, which rely on complete videos for static, post hoc analysis, online models process video streams incrementally and dynamically adap... | {
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2501.05515 | Neural Architecture Codesign for Fast Physics Applications | [
"cs.LG",
"cond-mat.mtrl-sci",
"hep-ex",
"physics.ins-det"
] | We develop a pipeline to streamline neural architecture codesign for physics applications to reduce the need for ML expertise when designing models for novel tasks. Our method employs neural architecture search and network compression in a two-stage approach to discover hardware efficient models. This approach consists... | {
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2501.05530 | Outlyingness Scores with Cluster Catch Digraphs | [
"stat.ML",
"cs.LG"
] | This paper introduces two novel, outlyingness scores (OSs) based on Cluster Catch Digraphs (CCDs): Outbound Outlyingness Score (OOS) and Inbound Outlyingness Score (IOS). These scores enhance the interpretability of outlier detection results. Both OSs employ graph-, density-, and distribution-based techniques, tailored... | {
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2501.05534 | OmniJet-${\alpha_{ C}}$: Learning point cloud calorimeter simulations
using generative transformers | [
"hep-ph",
"cs.LG",
"hep-ex",
"physics.ins-det"
] | We show the first use of generative transformers for generating calorimeter showers as point clouds in a high-granularity calorimeter. Using the tokenizer and generative part of the OmniJet-${\alpha}$ model, we represent the hits in the detector as sequences of integers. This model allows variable-length sequences, whi... | {
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2501.05541 | Customizable LLM-Powered Chatbot for Behavioral Science Research | [
"cs.LG"
] | The rapid advancement of Artificial Intelligence has resulted in the advent of Large Language Models (LLMs) with the capacity to produce text that closely resembles human communication. These models have been seamlessly integrated into diverse applications, enabling interactive and responsive communication across multi... | {
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2501.05548 | Switched Optimal Control with Dwell Time Constraints | [
"math.OC",
"cs.SY",
"eess.SY"
] | This paper presents an embedding-based approach for solving switched optimal control problems (SOCPs) with dwell time constraints. At first, an embedded optimal control problem (EOCP) is defined by replacing the discrete switching signal with a continuous embedded variable that can take intermediate values between the ... | {
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2501.05550 | Emergent weight morphologies in deep neural networks | [
"cs.LG",
"cond-mat.dis-nn"
] | Whether deep neural networks can exhibit emergent behaviour is not only relevant for understanding how deep learning works, it is also pivotal for estimating potential security risks of increasingly capable artificial intelligence systems. Here, we show that training deep neural networks gives rise to emergent weight m... | {
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2501.05552 | The dynamics of meaning through time: Assessment of Large Language
Models | [
"cs.CL",
"cs.AI"
] | Understanding how large language models (LLMs) grasp the historical context of concepts and their semantic evolution is essential in advancing artificial intelligence and linguistic studies. This study aims to evaluate the capabilities of various LLMs in capturing temporal dynamics of meaning, specifically how they int... | {
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2501.05554 | LLMQuoter: Enhancing RAG Capabilities Through Efficient Quote Extraction
From Large Contexts | [
"cs.CL",
"cs.AI"
] | We introduce LLMQuoter, a lightweight, distillation-based model designed to enhance Retrieval Augmented Generation (RAG) by extracting the most relevant textual evidence for downstream reasoning tasks. Built on the LLaMA-3B architecture and fine-tuned with Low-Rank Adaptation (LoRA) on a 15,000-sample subset of HotpotQ... | {
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2501.05555 | Improving Zero-Shot Object-Level Change Detection by Incorporating
Visual Correspondence | [
"cs.CV",
"cs.AI"
] | Detecting object-level changes between two images across possibly different views is a core task in many applications that involve visual inspection or camera surveillance. Existing change-detection approaches suffer from three major limitations: (1) lack of evaluation on image pairs that contain no changes, leading to... | {
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2501.05558 | Quantum Simplicial Neural Networks | [
"cs.NE"
] | Graph Neural Networks (GNNs) excel at learning from graph-structured data but are limited to modeling pairwise interactions, insufficient for capturing higher-order relationships present in many real-world systems. Topological Deep Learning (TDL) has allowed for systematic modeling of hierarchical higher-order interact... | {
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2501.05559 | Soup to go: mitigating forgetting during continual learning with model
averaging | [
"cs.LG",
"cs.AI"
] | In continual learning, where task data arrives in a sequence, fine-tuning on later tasks will often lead to performance degradation on earlier tasks. This is especially pronounced when these tasks come from diverse domains. In this setting, how can we mitigate catastrophic forgetting of earlier tasks and retain what th... | {
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2501.05563 | Prediction-Assisted Online Distributed Deep Learning Workload Scheduling
in GPU Clusters | [
"cs.DC",
"cs.LG"
] | The recent explosive growth of deep learning (DL) models has necessitated a compelling need for efficient job scheduling for distributed deep learning training with mixed parallelisms (DDLwMP) in GPU clusters. This paper proposes an adaptive shortest-remaining-processing-time-first (A-SRPT) scheduling algorithm, a nove... | {
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2501.05564 | Analog Bayesian neural networks are insensitive to the shape of the
weight distribution | [
"cs.LG",
"cs.AR",
"stat.ML"
] | Recent work has demonstrated that Bayesian neural networks (BNN's) trained with mean field variational inference (MFVI) can be implemented in analog hardware, promising orders of magnitude energy savings compared to the standard digital implementations. However, while Gaussians are typically used as the variational dis... | {
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2501.05566 | Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene
Understanding | [
"cs.CV",
"cs.AI",
"cs.CY"
] | Scene understanding is essential for enhancing driver safety, generating human-centric explanations for Automated Vehicle (AV) decisions, and leveraging Artificial Intelligence (AI) for retrospective driving video analysis. This study developed a dynamic scene retrieval system using Contrastive Language-Image Pretraini... | {
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2501.05567 | Approximate Supervised Object Distance Estimation on Unmanned Surface
Vehicles | [
"cs.CV",
"cs.AI"
] | Unmanned surface vehicles (USVs) and boats are increasingly important in maritime operations, yet their deployment is limited due to costly sensors and complexity. LiDAR, radar, and depth cameras are either costly, yield sparse point clouds or are noisy, and require extensive calibration. Here, we introduce a novel app... | {
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2501.05580 | Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics | [
"hep-lat",
"cs.LG",
"hep-ph",
"nucl-th"
] | The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex data sets. This is particularly relevant for quantum chromodynamics (QCD), the theory of strong interactions, with its inherent limita... | {
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2501.05583 | Learned Discrepancy Reconstruction and Benchmark Dataset for Magnetic
Particle Imaging | [
"math.NA",
"cs.LG",
"cs.NA"
] | Magnetic Particle Imaging (MPI) is an emerging imaging modality based on the magnetic response of superparamagnetic iron oxide nanoparticles to achieve high-resolution and real-time imaging without harmful radiation. One key challenge in the MPI image reconstruction task arises from its underlying noise model, which do... | {
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2501.05588 | Enforcing Fundamental Relations via Adversarial Attacks on Input
Parameter Correlations | [
"cs.LG",
"hep-ex"
] | Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature. For example, in high energy physics, one of the common deep learning use-cases is the classification of signal and background processes in particle collisions. ... | {
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2501.05590 | Negative Ties Highlight Hidden Extremes in Social Media Polarization | [
"physics.soc-ph",
"cs.SI"
] | Human interactions in the online world comprise a combination of positive and negative exchanges. These diverse interactions can be captured using signed network representations, where edges take positive or negative weights to indicate the sentiment of the interaction between individuals. Signed networks offer valuabl... | {
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2501.05591 | Session-Level Dynamic Ad Load Optimization using Offline Robust
Reinforcement Learning | [
"cs.LG"
] | Session-level dynamic ad load optimization aims to personalize the density and types of delivered advertisements in real time during a user's online session by dynamically balancing user experience quality and ad monetization. Traditional causal learning-based approaches struggle with key technical challenges, especial... | {
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2501.05593 | Bounds on Box Codes | [
"cs.IT",
"math.CO",
"math.IT"
] | Let $n_q(M,d)$ be the minimum length of a $q$-ary code of size $M$ and minimum distance $d$. Bounding $n_q(M,d)$ is a fundamental problem that lies at the heart of coding theory. This work considers a generalization $n^\bx_q(M,d)$ of $n_q(M,d)$ corresponding to codes in which codewords have \emph{protected} and \emph{u... | {
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2501.05601 | Exploring Large Language Models for Translating Romanian Computational
Problems into English | [
"cs.CL"
] | Recent studies have suggested that large language models (LLMs) underperform on mathematical and computer science tasks when these problems are translated from Romanian into English, compared to their original Romanian format. Accurate translation is critical for applications ranging from automatic translations in prog... | {
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2501.05605 | Advancing Personalized Learning Analysis via an Innovative Domain
Knowledge Informed Attention-based Knowledge Tracing Method | [
"cs.LG",
"cs.AI",
"cs.CY"
] | Emerging Knowledge Tracing (KT) models, particularly deep learning and attention-based Knowledge Tracing, have shown great potential in realizing personalized learning analysis via prediction of students' future performance based on their past interactions. The existing methods mainly focus on immediate past interactio... | {
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2501.05606 | Harmonizing Metadata of Language Resources for Enhanced Querying and
Accessibility | [
"cs.CL",
"cs.IR"
] | This paper addresses the harmonization of metadata from diverse repositories of language resources (LRs). Leveraging linked data and RDF techniques, we integrate data from multiple sources into a unified model based on DCAT and META-SHARE OWL ontology. Our methodology supports text-based search, faceted browsing, and a... | {
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2501.05610 | Towards Probabilistic Inference of Human Motor Intentions by Assistive
Mobile Robots Controlled via a Brain-Computer Interface | [
"cs.RO",
"cs.ET",
"cs.HC",
"cs.LG"
] | Assistive mobile robots are a transformative technology that helps persons with disabilities regain the ability to move freely. Although autonomous wheelchairs significantly reduce user effort, they still require human input to allow users to maintain control and adapt to changing environments. Brain Computer Interface... | {
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2501.05611 | Bit-depth color recovery via off-the-shelf super-resolution models | [
"eess.IV",
"cs.CV"
] | Advancements in imaging technology have enabled hardware to support 10 to 16 bits per channel, facilitating precise manipulation in applications like image editing and video processing. While deep neural networks promise to recover high bit-depth representations, existing methods often rely on scale-invariant image inf... | {
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2501.05614 | Watermarking Graph Neural Networks via Explanations for Ownership
Protection | [
"cs.CR",
"cs.AI"
] | Graph Neural Networks (GNNs) are the mainstream method to learn pervasive graph data and are widely deployed in industry, making their intellectual property valuable. However, protecting GNNs from unauthorized use remains a challenge. Watermarking, which embeds ownership information into a model, is a potential solutio... | {
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2501.05628 | Concerns and Values in Human-Robot Interactions: A Focus on Social
Robotics | [
"cs.RO",
"cs.HC"
] | Robots, as AI with physical instantiation, inhabit our social and physical world, where their actions have both social and physical consequences, posing challenges for researchers when designing social robots. This study starts with a scoping review to identify discussions and potential concerns arising from interactio... | {
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2501.05629 | The Impact of Model Scaling on Seen and Unseen Language Performance | [
"cs.CL",
"cs.AI"
] | The rapid advancement of Large Language Models (LLMs), particularly those trained on multilingual corpora, has intensified the need for a deeper understanding of their performance across a diverse range of languages and model sizes. Our research addresses this critical need by studying the performance and scaling behav... | {
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2501.05631 | HFMF: Hierarchical Fusion Meets Multi-Stream Models for Deepfake
Detection | [
"cs.CV"
] | The rapid progress in deep generative models has led to the creation of incredibly realistic synthetic images that are becoming increasingly difficult to distinguish from real-world data. The widespread use of Variational Models, Diffusion Models, and Generative Adversarial Networks has made it easier to generate convi... | {
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2501.05633 | Regularized Top-$k$: A Bayesian Framework for Gradient Sparsification | [
"cs.LG",
"cs.IT",
"eess.SP",
"math.IT"
] | Error accumulation is effective for gradient sparsification in distributed settings: initially-unselected gradient entries are eventually selected as their accumulated error exceeds a certain level. The accumulation essentially behaves as a scaling of the learning rate for the selected entries. Although this property p... | {
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2501.05635 | Enhancing Unsupervised Graph Few-shot Learning via Set Functions and
Optimal Transport | [
"cs.LG"
] | Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among researchers. Recent advancements in graph few-shot learning models have exhibited superior performance across diverse applications. Despite thei... | {
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2501.05636 | Identifying rich clubs in spatiotemporal interaction networks | [
"cs.SI",
"physics.soc-ph"
] | Spatial networks are widely used in various fields to represent and analyze interactions or relationships between locations or spatially distributed entities.There is a network science concept known as the 'rich club' phenomenon, which describes the tendency of 'rich' nodes to form densely interconnected sub-networks. ... | {
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2501.05639 | Scaling Safe Multi-Agent Control for Signal Temporal Logic
Specifications | [
"cs.MA",
"cs.RO"
] | Existing methods for safe multi-agent control using logic specifications like Signal Temporal Logic (STL) often face scalability issues. This is because they rely either on single-agent perspectives or on Mixed Integer Linear Programming (MILP)-based planners, which are complex to optimize. These methods have proven to... | {
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2501.05640 | Automating Date Format Detection for Data Visualization | [
"cs.CL"
] | Data preparation, specifically date parsing, is a significant bottleneck in analytic workflows. To address this, we present two algorithms, one based on minimum entropy and the other on natural language modeling that automatically derive date formats from string data. These algorithms achieve over 90% accuracy on a lar... | {
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2501.05643 | Iconicity in Large Language Models | [
"cs.CL",
"cs.AI"
] | Lexical iconicity, a direct relation between a word's meaning and its form, is an important aspect of every natural language, most commonly manifesting through sound-meaning associations. Since Large language models' (LLMs') access to both meaning and sound of text is only mediated (meaning through textual context, sou... | {
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2501.05644 | Interpretable Enzyme Function Prediction via Residue-Level Detection | [
"q-bio.BM",
"cs.LG"
] | Predicting multiple functions labeled with Enzyme Commission (EC) numbers from the enzyme sequence is of great significance but remains a challenge due to its sparse multi-label classification nature, i.e., each enzyme is typically associated with only a few labels out of more than 6000 possible EC numbers. However, ex... | {
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2501.05646 | Efficient Representations for High-Cardinality Categorical Variables in
Machine Learning | [
"cs.LG",
"cs.AI"
] | High\-cardinality categorical variables pose significant challenges in machine learning, particularly in terms of computational efficiency and model interpretability. Traditional one\-hot encoding often results in high\-dimensional sparse feature spaces, increasing the risk of overfitting and reducing scalability. This... | {
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2501.05647 | Collaboration of Large Language Models and Small Recommendation Models
for Device-Cloud Recommendation | [
"cs.IR",
"cs.AI",
"cs.CL",
"cs.DC"
] | Large Language Models (LLMs) for Recommendation (LLM4Rec) is a promising research direction that has demonstrated exceptional performance in this field. However, its inability to capture real-time user preferences greatly limits the practical application of LLM4Rec because (i) LLMs are costly to train and infer frequen... | {
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2501.05651 | A Practical Cross-Layer Approach for ML-Driven Storage Placement in
Warehouse-Scale Computers | [
"cs.DC",
"cs.LG"
] | Storage systems account for a major portion of the total cost of ownership (TCO) of warehouse-scale computers, and thus have a major impact on the overall system's efficiency. Machine learning (ML)-based methods for solving key problems in storage system efficiency, such as data placement, have shown significant promis... | {
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2501.05655 | Downlink Performance of Cell-Free Massive MIMO for LEO Satellite
Mega-Constellation | [
"eess.SP",
"cs.IT",
"cs.SY",
"eess.SY",
"math.IT"
] | Low-earth orbit (LEO) satellite communication (SatCom) has emerged as a promising technology for improving wireless connectivity in global areas. Cell-free massive multiple-input multiple-output (CF-mMIMO), an architecture recently proposed for next-generation networks, has yet to be fully explored for LEO satellites. ... | {
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2501.05656 | Evidential Deep Learning for Uncertainty Quantification and
Out-of-Distribution Detection in Jet Identification using Deep Neural
Networks | [
"hep-ex",
"cs.LG"
] | Current methods commonly used for uncertainty quantification (UQ) in deep learning (DL) models utilize Bayesian methods which are computationally expensive and time-consuming. In this paper, we provide a detailed study of UQ based on evidential deep learning (EDL) for deep neural network models designed to identify jet... | {
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2501.05660 | Fully Decentralized Computation Offloading in Priority-Driven Edge
Computing Systems | [
"cs.IT",
"cs.GT",
"cs.SY",
"eess.SY",
"math.IT"
] | We develop a novel framework for fully decentralized offloading policy design in multi-access edge computing (MEC) systems. The system comprises $N$ power-constrained user equipments (UEs) assisted by an edge server (ES) to process incoming tasks. Tasks are labeled with urgency flags, and in this paper, we classify the... | {
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2501.05661 | TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation
Learning | [
"cs.LG"
] | We propose TAMER, a Test-time Adaptive MoE-driven framework for EHR Representation learning. TAMER combines a Mixture-of-Experts (MoE) with Test-Time Adaptation (TTA) to address two critical challenges in EHR modeling: patient population heterogeneity and distribution shifts. The MoE component handles diverse patient s... | {
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2501.05662 | Cascaded Self-Evaluation Augmented Training for Efficient Multimodal
Large Language Models | [
"cs.CL",
"cs.AI"
] | Efficient Multimodal Large Language Models (EMLLMs) have rapidly advanced recently. Incorporating Chain-of-Thought (CoT) reasoning and step-by-step self-evaluation has improved their performance. However, limited parameters often hinder EMLLMs from effectively using self-evaluation during inference. Key challenges incl... | {
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2501.05663 | Learning to Measure Quantum Neural Networks | [
"quant-ph",
"cs.AI",
"cs.ET",
"cs.LG",
"cs.NE"
] | The rapid progress in quantum computing (QC) and machine learning (ML) has attracted growing attention, prompting extensive research into quantum machine learning (QML) algorithms to solve diverse and complex problems. Designing high-performance QML models demands expert-level proficiency, which remains a significant o... | {
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2501.05667 | TransPlace: Transferable Circuit Global Placement via Graph Neural
Network | [
"cs.LG",
"cs.AI",
"cs.AR"
] | Global placement, a critical step in designing the physical layout of computer chips, is essential to optimize chip performance. Prior global placement methods optimize each circuit design individually from scratch. Their neglect of transferable knowledge limits solution efficiency and chip performance as circuit compl... | {
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2501.05669 | LPRnet: A self-supervised registration network for LiDAR and
photogrammetric point clouds | [
"cs.CV",
"eess.IV"
] | LiDAR and photogrammetry are active and passive remote sensing techniques for point cloud acquisition, respectively, offering complementary advantages and heterogeneous. Due to the fundamental differences in sensing mechanisms, spatial distributions and coordinate systems, their point clouds exhibit significant discrep... | {
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2501.05673 | Network Diffuser for Placing-Scheduling Service Function Chains with
Inverse Demonstration | [
"cs.NI",
"cs.AI"
] | Network services are increasingly managed by considering chained-up virtual network functions and relevant traffic flows, known as the Service Function Chains (SFCs). To deal with sequential arrivals of SFCs in an online fashion, we must consider two closely-coupled problems - an SFC placement problem that maps SFCs to... | {
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2501.05675 | Synergizing Large Language Models and Task-specific Models for Time
Series Anomaly Detection | [
"cs.AI",
"cs.LG"
] | In anomaly detection, methods based on large language models (LLMs) can incorporate expert knowledge by reading professional document, while task-specific small models excel at extracting normal data patterns and detecting value fluctuations from training data of target applications. Inspired by the human nervous syste... | {
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2501.05680 | EXION: Exploiting Inter- and Intra-Iteration Output Sparsity for
Diffusion Models | [
"cs.AR",
"cs.AI",
"cs.LG"
] | Over the past few years, diffusion models have emerged as novel AI solutions, generating diverse multi-modal outputs from text prompts. Despite their capabilities, they face challenges in computing, such as excessive latency and energy consumption due to their iterative architecture. Although prior works specialized in... | {
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2501.05684 | Data driven discovery of human mobility models | [
"physics.soc-ph",
"cs.NE"
] | Human mobility is a fundamental aspect of social behavior, with broad applications in transportation, urban planning, and epidemic modeling. However, for decades new mathematical formulas to model mobility phenomena have been scarce and usually discovered by analogy to physical processes, such as the gravity model and ... | {
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2501.05686 | Deep Reversible Consistency Learning for Cross-modal Retrieval | [
"cs.CV",
"cs.MM"
] | Cross-modal retrieval (CMR) typically involves learning common representations to directly measure similarities between multimodal samples. Most existing CMR methods commonly assume multimodal samples in pairs and employ joint training to learn common representations, limiting the flexibility of CMR. Although some meth... | {
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2501.05687 | UniQ: Unified Decoder with Task-specific Queries for Efficient Scene
Graph Generation | [
"cs.CV"
] | Scene Graph Generation(SGG) is a scene understanding task that aims at identifying object entities and reasoning their relationships within a given image. In contrast to prevailing two-stage methods based on a large object detector (e.g., Faster R-CNN), one-stage methods integrate a fixed-size set of learnable queries ... | {
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2501.05688 | eKalibr: Dynamic Intrinsic Calibration for Event Cameras From First
Principles of Events | [
"cs.CV",
"cs.RO"
] | The bio-inspired event camera has garnered extensive research attention in recent years, owing to its significant potential derived from its high dynamic range and low latency characteristics. Similar to the standard camera, the event camera requires precise intrinsic calibration to facilitate further high-level visual... | {
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2501.05690 | Overcoming Language Priors for Visual Question Answering Based on
Knowledge Distillation | [
"cs.CV",
"cs.CL"
] | Previous studies have pointed out that visual question answering (VQA) models are prone to relying on language priors for answer predictions. In this context, predictions often depend on linguistic shortcuts rather than a comprehensive grasp of multimodal knowledge, which diminishes their generalization ability. In thi... | {
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2501.05700 | Linguistic Entity Masking to Improve Cross-Lingual Representation of
Multilingual Language Models for Low-Resource Languages | [
"cs.CL"
] | Multilingual Pre-trained Language models (multiPLMs), trained on the Masked Language Modelling (MLM) objective are commonly being used for cross-lingual tasks such as bitext mining. However, the performance of these models is still suboptimal for low-resource languages (LRLs). To improve the language representation of ... | {
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2501.05707 | Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models (LLMs) have achieved remarkable performance in recent years but are fundamentally limited by the underlying training data. To improve models beyond the training data, recent works have explored how LLMs can be used to generate synthetic data for autonomous self-improvement. However, successive ste... | {
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2501.05708 | Differential Properties of Information in Jump-diffusion Channels | [
"cs.IT",
"math.IT"
] | We propose a channel modeling using jump-diffusion processes, and study the differential properties of entropy and mutual information. By utilizing the Kramers-Moyal and Kolmogorov-Feller equations, we express the mutual information between the input and the output in series and integral forms, presented by Fisher-type... | {
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} |
2501.05710 | EmotiCrafter: Text-to-Emotional-Image Generation based on
Valence-Arousal Model | [
"cs.CV"
] | Recent research shows that emotions can enhance users' cognition and influence information communication. While research on visual emotion analysis is extensive, limited work has been done on helping users generate emotionally rich image content. Existing work on emotional image generation relies on discrete emotion ca... | {
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} |
2501.05711 | From My View to Yours: Ego-Augmented Learning in Large Vision Language
Models for Understanding Exocentric Daily Living Activities | [
"cs.CV"
] | Large Vision Language Models (LVLMs) have demonstrated impressive capabilities in video understanding, yet their adoption for Activities of Daily Living (ADL) remains limited by their inability to capture fine-grained interactions and spatial relationships. This limitation is particularly evident in ADL tasks, where un... | {
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2501.05712 | Multi-Step Reasoning in Korean and the Emergent Mirage | [
"cs.CL"
] | We introduce HRMCR (HAE-RAE Multi-Step Commonsense Reasoning), a benchmark designed to evaluate large language models' ability to perform multi-step reasoning in culturally specific contexts, focusing on Korean. The questions are automatically generated via templates and algorithms, requiring LLMs to integrate Korean c... | {
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} |
2501.05714 | How to Enable Effective Cooperation Between Humans and NLP Models: A
Survey of Principles, Formalizations, and Beyond | [
"cs.CL",
"cs.AI",
"cs.HC"
] | With the advancement of large language models (LLMs), intelligent models have evolved from mere tools to autonomous agents with their own goals and strategies for cooperating with humans. This evolution has birthed a novel paradigm in NLP, i.e., human-model cooperation, that has yielded remarkable progress in numerous ... | {
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} |
2501.05715 | Non-intrusive Data-driven ADI-based Low-rank Balanced Truncation | [
"eess.SY",
"cs.SY"
] | In this short note, a non-intrusive data-driven formulation of ADI-based low-rank balanced truncation is provided. The proposed algorithm only requires transfer function samples at the mirror images of ADI shifts. If some shifts are used in both approximating the controllability Gramian and the observability Gramian, t... | {
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} |
2501.05717 | Zero-shot Shark Tracking and Biometrics from Aerial Imagery | [
"cs.CV",
"cs.AI",
"q-bio.QM"
] | The recent widespread adoption of drones for studying marine animals provides opportunities for deriving biological information from aerial imagery. The large scale of imagery data acquired from drones is well suited for machine learning (ML) analysis. Development of ML models for analyzing marine animal aerial imagery... | {
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} |
2501.05718 | Performance Analysis of Perturbation-enhanced SC decoders | [
"cs.IT",
"math.IT"
] | In this paper, we analyze the delay probability of the first error position in perturbation-enhanced Successive cancellation (SC) decoding for polar codes. Our findings reveal that, asymptotically, an SC decoder's performance does not degrade after one perturbation, and it improves with a probability of $\frac{1}{2}$. ... | {
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} |
2501.05723 | Robot Error Awareness Through Human Reactions: Implementation,
Evaluation, and Recommendations | [
"cs.RO",
"cs.HC"
] | Effective error detection is crucial to prevent task disruption and maintain user trust. Traditional methods often rely on task-specific models or user reporting, which can be inflexible or slow. Recent research suggests social signals, naturally exhibited by users in response to robot errors, can enable more flexible,... | {
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} |
2501.05727 | Enabling Scalable Oversight via Self-Evolving Critic | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Despite their remarkable performance, the development of Large Language Models (LLMs) faces a critical challenge in scalable oversight: providing effective feedback for tasks where human evaluation is difficult or where LLMs outperform humans. While there is growing interest in using LLMs for critique, current approach... | {
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} |
2501.05728 | Super-class guided Transformer for Zero-Shot Attribute Classification | [
"cs.CV"
] | Attribute classification is crucial for identifying specific characteristics within image regions. Vision-Language Models (VLMs) have been effective in zero-shot tasks by leveraging their general knowledge from large-scale datasets. Recent studies demonstrate that transformer-based models with class-wise queries can ef... | {
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} |
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