id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.02140 | SparseGrasp: Robotic Grasping via 3D Semantic Gaussian Splatting from
Sparse Multi-View RGB Images | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Language-guided robotic grasping is a rapidly advancing field where robots are instructed using human language to grasp specific objects. However, existing methods often depend on dense camera views and struggle to quickly update scenes, limiting their effectiveness in changeable environments. In contrast, we propose... | {
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2412.02141 | WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image | [
"cs.CV",
"cs.CL"
] | Recent advancements in computational pathology have produced patch-level Multi-modal Large Language Models (MLLMs), but these models are limited by their inability to analyze whole slide images (WSIs) comprehensively and their tendency to bypass crucial morphological features that pathologists rely on for diagnosis. To... | {
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2412.02142 | Personalized Multimodal Large Language Models: A Survey | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.IR"
] | Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as text, images, and audio, to perform complex tasks with high accuracy. This paper presents a comprehensive survey on personalized multimodal lar... | {
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2412.02145 | Effective Mitigations for Systemic Risks from General-Purpose AI | [
"cs.CY",
"cs.AI"
] | The systemic risks posed by general-purpose AI models are a growing concern, yet the effectiveness of mitigations remains underexplored. Previous research has proposed frameworks for risk mitigation, but has left gaps in our understanding of the perceived effectiveness of measures for mitigating systemic risks. Our stu... | {
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2412.02146 | Distributed Task Allocation for Multi-Agent Systems: A Submodular
Optimization Approach | [
"cs.MA"
] | This paper investigates dynamic task allocation for multi-agent systems (MASs) under resource constraints, with a focus on maximizing the global utility of agents while ensuring a conflict-free allocation of targets. We present a more adaptable submodular maximization framework for the MAS task allocation under resourc... | {
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2412.02148 | Mining Tweets to Predict Future Bitcoin Price | [
"cs.AI"
] | Bitcoin has increased investment interests in people during the last decade. We have seen an increase in the number of posts on social media platforms about cryptocurrency, especially Bitcoin. This project focuses on analyzing user tweet data in combination with Bitcoin price data to see the relevance between price flu... | {
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2412.02149 | Leveraging Large Language Models for Comparative Literature
Summarization with Reflective Incremental Mechanisms | [
"cs.CL",
"cs.IR"
] | In this paper, we introduce ChatCite, a novel method leveraging large language models (LLMs) for generating comparative literature summaries. The ability to summarize research papers with a focus on key comparisons between studies is an essential task in academic research. Existing summarization models, while effective... | {
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2412.02153 | Revisiting the Initial Steps in Adaptive Gradient Descent Optimization | [
"cs.LG",
"cs.AI"
] | Adaptive gradient optimization methods, such as Adam, are prevalent in training deep neural networks across diverse machine learning tasks due to their ability to achieve faster convergence. However, these methods often suffer from suboptimal generalization compared to stochastic gradient descent (SGD) and exhibit inst... | {
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2412.02154 | Failure Probability Estimation for Black-Box Autonomous Systems using
State-Dependent Importance Sampling Proposals | [
"cs.RO",
"cs.AI",
"cs.LG",
"stat.ML"
] | Estimating the probability of failure is a critical step in developing safety-critical autonomous systems. Direct estimation methods such as Monte Carlo sampling are often impractical due to the rarity of failures in these systems. Existing importance sampling approaches do not scale to sequential decision-making syste... | {
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2412.02155 | CausalMob: Causal Human Mobility Prediction with LLMs-derived Human
Intentions toward Public Events | [
"cs.LG",
"cs.AI",
"cs.IR",
"cs.SI"
] | Large-scale human mobility exhibits spatial and temporal patterns that can assist policymakers in decision making. Although traditional prediction models attempt to capture these patterns, they often interfered by non-periodic public events, such as disasters and occasional celebrations. Since regular human mobility pa... | {
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2412.02158 | Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural
Pests and Diseases | [
"cs.CV"
] | In the general domain, large multimodal models (LMMs) have achieved significant advancements, yet challenges persist in applying them to specific fields, especially agriculture. As the backbone of the global economy, agriculture confronts numerous challenges, with pests and diseases being particularly concerning due to... | {
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2412.02159 | Jailbreak Defense in a Narrow Domain: Limitations of Existing Methods
and a New Transcript-Classifier Approach | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CR"
] | Defending large language models against jailbreaks so that they never engage in a broadly-defined set of forbidden behaviors is an open problem. In this paper, we investigate the difficulty of jailbreak-defense when we only want to forbid a narrowly-defined set of behaviors. As a case study, we focus on preventing an L... | {
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2412.02161 | Towards the efficacy of federated prediction for epidemics on networks | [
"cs.SI",
"cs.DC",
"cs.LG"
] | Epidemic prediction is of practical significance in public health, enabling early intervention, resource allocation, and strategic planning. However, privacy concerns often hinder the sharing of health data among institutions, limiting the development of accurate prediction models. In this paper, we develop a general p... | {
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2412.02164 | A Theoretical Framework for Acoustic Neighbor Embeddings | [
"eess.AS",
"cs.CL",
"cs.SD"
] | This paper provides a theoretical framework for interpreting acoustic neighbor embeddings, which are representations of the phonetic content of variable-width audio or text in a fixed-dimensional embedding space. A probabilistic interpretation of the distances between embeddings is proposed, based on a general quantita... | {
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2412.02166 | Analyzing the Impact of AI Tools on Student Study Habits and Academic
Performance | [
"cs.AI"
] | This study explores the effectiveness of AI tools in enhancing student learning, specifically in improving study habits, time management, and feedback mechanisms. The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis. Student feedback... | {
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2412.02168 | Generative Photography: Scene-Consistent Camera Control for Realistic
Text-to-Image Synthesis | [
"cs.CV"
] | Image generation today can produce somewhat realistic images from text prompts. However, if one asks the generator to synthesize a particular camera setting such as creating different fields of view using a 24mm lens versus a 70mm lens, the generator will not be able to interpret and generate scene-consistent images. T... | {
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2412.02171 | Underload: Defending against Latency Attacks for Object Detectors on
Edge Devices | [
"cs.CV",
"cs.CR"
] | Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorithmic backbone of neural networks is brittle to imperceptible perturbations in the system inputs, which were generally known as misclassifyin... | {
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2412.02172 | VISCO: Benchmarking Fine-Grained Critique and Correction Towards
Self-Improvement in Visual Reasoning | [
"cs.CV",
"cs.AI",
"cs.CL"
] | The ability of large vision-language models (LVLMs) to critique and correct their reasoning is an essential building block towards their self-improvement. However, a systematic analysis of such capabilities in LVLMs is still lacking. We propose VISCO, the first benchmark to extensively analyze the fine-grained critique... | {
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2412.02173 | Keeping Experts in the Loop: Expert-Guided Optimization for Clinical
Data Classification using Large Language Models | [
"cs.AI"
] | Since the emergence of Large Language Models (LLMs), the challenge of effectively leveraging their potential in healthcare has taken center stage. A critical barrier to using LLMs for extracting insights from unstructured clinical notes lies in the prompt engineering process. Despite its pivotal role in determining tas... | {
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2412.02175 | Improved Complexity for Smooth Nonconvex Optimization: A Two-Level
Online Learning Approach with Quasi-Newton Methods | [
"math.OC",
"cs.LG",
"stat.ML"
] | We study the problem of finding an $\epsilon$-first-order stationary point (FOSP) of a smooth function, given access only to gradient information. The best-known gradient query complexity for this task, assuming both the gradient and Hessian of the objective function are Lipschitz continuous, is ${O}(\epsilon^{-7/4})$.... | {
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2412.02176 | Self-Supervised Learning-Based Path Planning and Obstacle Avoidance
Using PPO and B-Splines in Unknown Environments | [
"cs.RO",
"cs.AI"
] | This paper introduces SmartBSP, an advanced self-supervised learning framework for real-time path planning and obstacle avoidance in autonomous robotics navigating through complex environments. The proposed system integrates Proximal Policy Optimization (PPO) with Convolutional Neural Networks (CNN) and Actor-Critic ar... | {
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2412.02177 | Anatomically-Grounded Fact Checking of Automated Chest X-ray Reports | [
"cs.CV",
"cs.AI"
] | With the emergence of large-scale vision-language models, realistic radiology reports may be generated using only medical images as input guided by simple prompts. However, their practical utility has been limited due to the factual errors in their description of findings. In this paper, we propose a novel model for ex... | {
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2412.02181 | Generalizing Weisfeiler-Lehman Kernels to Subgraphs | [
"cs.LG",
"cs.AI",
"cs.SI"
] | Subgraph representation learning has been effective in solving various real-world problems. However, current graph neural networks (GNNs) produce suboptimal results for subgraph-level tasks due to their inability to capture complex interactions within and between subgraphs. To provide a more expressive and efficient al... | {
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2412.02186 | VideoICL: Confidence-based Iterative In-context Learning for
Out-of-Distribution Video Understanding | [
"cs.CV",
"cs.AI"
] | Recent advancements in video large multimodal models (LMMs) have significantly improved their video understanding and reasoning capabilities. However, their performance drops on out-of-distribution (OOD) tasks that are underrepresented in training data. Traditional methods like fine-tuning on OOD datasets are impractic... | {
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2412.02187 | Deep Learning, Machine Learning, Advancing Big Data Analytics and
Management | [
"cs.LG"
] | Advancements in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management into pivotal domains for research and application. This work explores the theoretical foundations, methodological advancements, and practical implementations of these techn... | {
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2412.02189 | Comparative Performance of Machine Learning Algorithms for Early Genetic
Disorder and Subclass Classification | [
"cs.AI"
] | A great deal of effort has been devoted to discovering a particular genetic disorder, but its classification across a broad spectrum of disorder classes and types remains elusive. Early diagnosis of genetic disorders enables timely interventions and improves outcomes. This study implements machine learning models using... | {
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2412.02192 | Thallus: An RDMA-based Columnar Data Transport Protocol | [
"cs.DC",
"cs.DB",
"cs.OS"
] | The volume of data generated and stored in contemporary global data centers is experiencing exponential growth. This rapid data growth necessitates efficient processing and analysis to extract valuable business insights. In distributed data processing systems, data undergoes exchanges between the compute servers that c... | {
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2412.02193 | LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language
Models | [
"cs.CV",
"cs.AI"
] | Open-universe 3D layout generation arranges unlabeled 3D assets conditioned on language instruction. Large language models (LLMs) struggle with generating physically plausible 3D scenes and adherence to input instructions, particularly in cluttered scenes. We introduce LayoutVLM, a framework and scene layout representa... | {
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2412.02194 | Stimulated Raman Scattering in Nonlinear Silicon Nanophotonic
Waveguides: Theory and Applications in Photonic Integrated Circuits | [
"physics.optics",
"cs.SY",
"eess.SP",
"eess.SY",
"physics.app-ph"
] | Photonics caught world attention since channel capacity limit of metallic interconnects approached due to research and design in high speed digital processors. Use of dielectrics, instead, suitable for light propagation was more attractive due to its extremely wide bandwidth. Many of the devices, both active and passiv... | {
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2412.02196 | SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph
Neural Architecture Search | [
"cs.LG"
] | GNAS (Graph Neural Architecture Search) has demonstrated great effectiveness in automatically designing the optimal graph neural architectures for multiple downstream tasks, such as node classification and link prediction. However, most existing GNAS methods cannot efficiently handle large-scale graphs containing more ... | {
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2412.02197 | Cascaded Multi-Scale Attention for Enhanced Multi-Scale Feature
Extraction and Interaction with Low-Resolution Images | [
"cs.CV"
] | In real-world applications of image recognition tasks, such as human pose estimation, cameras often capture objects, like human bodies, at low resolutions. This scenario poses a challenge in extracting and leveraging multi-scale features, which is often essential for precise inference. To address this challenge, we pro... | {
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2412.02198 | Transformer-Based Auxiliary Loss for Face Recognition Across Age
Variations | [
"cs.CV"
] | Aging presents a significant challenge in face recognition, as changes in skin texture and tone can alter facial features over time, making it particularly difficult to compare images of the same individual taken years apart, such as in long-term identification scenarios. Transformer networks have the strength to prese... | {
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2412.02202 | 3D representation in 512-Byte:Variational tokenizer is the key for
autoregressive 3D generation | [
"cs.CV"
] | Autoregressive transformers have revolutionized high-fidelity image generation. One crucial ingredient lies in the tokenizer, which compresses high-resolution image patches into manageable discrete tokens with a scanning or hierarchical order suitable for large language models. Extending these tokenizers to 3D generati... | {
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2412.02205 | DataLab: A Unified Platform for LLM-Powered Business Intelligence | [
"cs.DB",
"cs.AI",
"cs.CL"
] | Business intelligence (BI) transforms large volumes of data within modern organizations into actionable insights for informed decision-making. Recently, large language model (LLM)-based agents have streamlined the BI workflow by automatically performing task planning, reasoning, and actions in executable environments b... | {
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2412.02210 | CC-OCR: A Comprehensive and Challenging OCR Benchmark for Evaluating
Large Multimodal Models in Literacy | [
"cs.CV"
] | Large Multimodal Models (LMMs) have demonstrated impressive performance in recognizing document images with natural language instructions. However, it remains unclear to what extent capabilities in literacy with rich structure and fine-grained visual challenges. The current landscape lacks a comprehensive benchmark to ... | {
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2412.02211 | An Automated Data Mining Framework Using Autoencoders for Feature
Extraction and Dimensionality Reduction | [
"cs.LG"
] | This study proposes an automated data mining framework based on autoencoders and experimentally verifies its effectiveness in feature extraction and data dimensionality reduction. Through the encoding-decoding structure, the autoencoder can capture the data's potential characteristics and achieve noise reduction and an... | {
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2412.02214 | GIST: Towards Photorealistic Style Transfer via Multiscale Geometric
Representations | [
"cs.CV",
"eess.IV"
] | State-of-the-art Style Transfer methods often leverage pre-trained encoders optimized for discriminative tasks, which may not be ideal for image synthesis. This can result in significant artifacts and loss of photorealism. Motivated by the ability of multiscale geometric image representations to capture fine-grained de... | {
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2412.02215 | Recovering implicit physics model under real-world constraints | [
"cs.LG",
"cs.AI"
] | Recovering a physics-driven model, i.e. a governing set of equations of the underlying dynamical systems, from the real-world data has been of recent interest. Most existing methods either operate on simulation data with unrealistically high sampling rates or require explicit measurements of all system variables, which... | {
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2412.02220 | Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models
by Recycling Pre-Tuned LoRAs | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) such as ChatGPT demonstrate strong few-shot adaptability without requiring fine-tuning, positioning them ideal for data-limited and real-time applications. However, this adaptability has not yet been replicated in current Visual Foundation Models (VFMs), which require explicit fine-tuning w... | {
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2412.02222 | Deep learning approach for predicting the replicator equation in
evolutionary game theory | [
"cs.AI"
] | This paper presents a physics-informed deep learning approach for predicting the replicator equation, allowing accurate forecasting of population dynamics. This methodological innovation allows us to derive governing differential or difference equations for systems that lack explicit mathematical models. We used the SI... | {
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2412.02224 | 3D Modular Microrobots: Micro-Origami Cubes with Integrated Si Chips
Dive, Communicate, Flash Programs, and Form Collectives | [
"eess.SY",
"cs.SY"
] | Modular microrobotics can potentially address many information-intensive microtasks in medicine, manufacturing and the environment. However, surface area has limited the natural powering, communication, functional integration, and self-assembly of smart mass-fabricated modular robotic devices at small scales. We demons... | {
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2412.02225 | How to Use Diffusion Priors under Sparse Views? | [
"cs.CV"
] | Novel view synthesis under sparse views has been a long-term important challenge in 3D reconstruction. Existing works mainly rely on introducing external semantic or depth priors to supervise the optimization of 3D representations. However, the diffusion model, as an external prior that can directly provide visual supe... | {
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2412.02228 | BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection stage and unaligned entity prototypes in the type classification stage persist. Additionally, LLMs have not proven to be effective few-shot i... | {
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2412.02230 | Learning from Concealed Labels | [
"cs.LG"
] | Annotating data for sensitive labels (e.g., disease, smoking) poses a potential threats to individual privacy in many real-world scenarios. To cope with this problem, we propose a novel setting to protect privacy of each instance, namely learning from concealed labels for multi-class classification. Concealed labels pr... | {
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2412.02234 | CubeFormer: A Simple yet Effective Baseline for Lightweight Image
Super-Resolution | [
"cs.CV"
] | Lightweight image super-resolution (SR) methods aim at increasing the resolution and restoring the details of an image using a lightweight neural network. However, current lightweight SR methods still suffer from inferior performance and unpleasant details. Our analysis reveals that these methods are hindered by constr... | {
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2412.02237 | Cross-Attention Head Position Patterns Can Align with Human Visual
Concepts in Text-to-Image Generative Models | [
"cs.CV",
"cs.AI"
] | Recent text-to-image diffusion models leverage cross-attention layers, which have been effectively utilized to enhance a range of visual generative tasks. However, our understanding of cross-attention layers remains somewhat limited. In this study, we introduce a mechanistic interpretability approach for diffusion mode... | {
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2412.02238 | Exponential Stabilization of Linear Systems using Nearest-Action Control
with Countable Input Set | [
"math.OC",
"cs.SY",
"eess.SY"
] | This paper studies stabilization of linear time-invariant (LTI) systems when control actions can only be realized in finitely many directions where it is possible to actuate uniformly or logarithmically extended positive scaling factors in each direction. Furthermore, a nearest-action selection approach is used to map ... | {
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2412.02240 | ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning | [
"cs.LG"
] | Learning from Multi-Positive and Unlabeled (MPU) data has gradually attracted significant attention from practical applications. Unfortunately, the risk of MPU also suffer from the shift of minimum risk, particularly when the models are very flexible as shown in Fig.\ref{moti}. In this paper, to alleviate the shifting ... | {
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2412.02241 | Fast LiDAR Data Generation with Rectified Flows | [
"cs.CV",
"cs.RO"
] | Building LiDAR generative models holds promise as powerful data priors for restoration, scene manipulation, and scalable simulation in autonomous mobile robots. In recent years, approaches using diffusion models have emerged, significantly improving training stability and generation quality. Despite the success of diff... | {
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2412.02242 | U-Net in Medical Image Segmentation: A Review of Its Applications Across
Modalities | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Medical imaging is essential in healthcare to provide key insights into patient anatomy and pathology, aiding in diagnosis and treatment. Non-invasive techniques such as X-ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US), capture detailed images of organs, tissues, and abnormalities.... | {
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2412.02244 | On Simplifying Large-Scale Spatial Vectors: Fast, Memory-Efficient, and
Cost-Predictable k-means | [
"cs.LG"
] | The k-means algorithm can simplify large-scale spatial vectors, such as 2D geo-locations and 3D point clouds, to support fast analytics and learning. However, when processing large-scale datasets, existing k-means algorithms have been developed to achieve high performance with significant computational resources, such ... | {
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2412.02245 | SparseLGS: Sparse View Language Embedded Gaussian Splatting | [
"cs.CV"
] | Recently, several studies have combined Gaussian Splatting to obtain scene representations with language embeddings for open-vocabulary 3D scene understanding. While these methods perform well, they essentially require very dense multi-view inputs, limiting their applicability in real-world scenarios. In this work, we ... | {
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2412.02247 | Development and Performance of a Static Pluviometer System | [
"eess.SY",
"cs.SY"
] | As the frequency and severity of climate-related events such as droughts, floods, and water scarcity continue to escalate, accurate rainfall monitoring becomes increasingly critical. This paper covers various industry methods of measuring rainfall as well as our own ground pluviometer system. Our system consists of an ... | {
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2412.02249 | Multi-robot autonomous 3D reconstruction using Gaussian splatting with
Semantic guidance | [
"cs.RO",
"cs.CV"
] | Implicit neural representations and 3D Gaussian splatting (3DGS) have shown great potential for scene reconstruction. Recent studies have expanded their applications in autonomous reconstruction through task assignment methods. However, these methods are mainly limited to single robot, and rapid reconstruction of large... | {
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2412.02250 | Vision Transformers for Weakly-Supervised Microorganism Enumeration | [
"cs.CV"
] | Microorganism enumeration is an essential task in many applications, such as assessing contamination levels or ensuring health standards when evaluating surface cleanliness. However, it's traditionally performed by human-supervised methods that often require manual counting, making it tedious and time-consuming. Previo... | {
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2412.02251 | Selective Reviews of Bandit Problems in AI via a Statistical View | [
"stat.ML",
"cs.AI",
"cs.LG",
"econ.EM",
"math.PR"
] | Reinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment. A key subset includes stochastic multi-armed bandit (MAB) and continuum-armed bandit (SCAB) problems, which model sequential decision-making und... | {
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2412.02252 | Compressing KV Cache for Long-Context LLM Inference with Inter-Layer
Attention Similarity | [
"cs.CL"
] | The increasing context window size in Large Language Models (LLMs), such as the GPT and LLaMA series, has improved their ability to tackle complex, long-text tasks, but at the cost of inference efficiency, particularly regarding memory and computational complexity. Existing methods, including selective token retention ... | {
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2412.02254 | ProbPose: A Probabilistic Approach to 2D Human Pose Estimation | [
"cs.CV"
] | Current Human Pose Estimation methods have achieved significant improvements. However, state-of-the-art models ignore out-of-image keypoints and use uncalibrated heatmaps as keypoint location representations. To address these limitations, we propose ProbPose, which predicts for each keypoint: a calibrated probability o... | {
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2412.02259 | VideoGen-of-Thought: A Collaborative Framework for Multi-Shot Video
Generation | [
"cs.CV",
"cs.AI"
] | Current video generation models excel at generating short clips but still struggle with creating multi-shot, movie-like videos. Existing models trained on large-scale data on the back of rich computational resources are unsurprisingly inadequate for maintaining a logical storyline and visual consistency across multiple... | {
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2412.02260 | BiCSI: A Binary Encoding and Fingerprint-Based Matching Algorithm for
Wi-Fi Indoor Positioning | [
"eess.SP",
"cs.IT",
"math.IT"
] | Traditional global positioning systems often underperform indoors, whereas Wi-Fi has become an effective medium for various radio sensing services. Specifically, utilizing channel state information (CSI) from Wi-Fi networks provides a non-contact method for precise indoor positioning; yet, accurately interpreting the c... | {
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2412.02261 | Diffusion Implicit Policy for Unpaired Scene-aware Motion Synthesis | [
"cs.CV"
] | Human motion generation is a long-standing problem, and scene-aware motion synthesis has been widely researched recently due to its numerous applications. Prevailing methods rely heavily on paired motion-scene data whose quantity is limited. Meanwhile, it is difficult to generalize to diverse scenes when trained only o... | {
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2412.02262 | Composing Open-domain Vision with RAG for Ocean Monitoring and
Conservation | [
"cs.CV",
"cs.LG"
] | Climate change's destruction of marine biodiversity is threatening communities and economies around the world which rely on healthy oceans for their livelihoods. The challenge of applying computer vision to niche, real-world domains such as ocean conservation lies in the dynamic and diverse environments where tradition... | {
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2412.02263 | Connecting Large Language Models with Blockchain: Advancing the
Evolution of Smart Contracts from Automation to Intelligence | [
"cs.DC",
"cs.AI"
] | Blockchain smart contracts have catalyzed the development of decentralized applications across various domains, including decentralized finance. However, due to constraints in computational resources and the prevalence of data silos, current smart contracts face significant challenges in fully leveraging the powerful c... | {
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2412.02264 | Technical Report on Reinforcement Learning Control on the Lucas-N\"ulle
Inverted Pendulum | [
"eess.SY",
"cs.DC",
"cs.LG",
"cs.SY"
] | The discipline of automatic control is making increased use of concepts that originate from the domain of machine learning. Herein, reinforcement learning (RL) takes an elevated role, as it is inherently designed for sequential decision making, and can be applied to optimal control problems without the need for a plant... | {
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2412.02265 | Diabetic Retinopathy Classification from Retinal Images using Machine
Learning Approaches | [
"cs.CV",
"cs.LG"
] | Diabetic Retinopathy is one of the most familiar diseases and is a diabetes complication that affects eyes. Initially, diabetic retinopathy may cause no symptoms or only mild vision problems. Eventually, it can cause blindness. So early detection of symptoms could help to avoid blindness. In this paper, we present some... | {
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2412.02266 | BOTracle: A framework for Discriminating Bots and Humans | [
"cs.LG"
] | Bots constitute a significant portion of Internet traffic and are a source of various issues across multiple domains. Modern bots often become indistinguishable from real users, as they employ similar methods to browse the web, including using real browsers. We address the challenge of bot detection in high-traffic sce... | {
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2412.02267 | GSGTrack: Gaussian Splatting-Guided Object Pose Tracking from RGB Videos | [
"cs.CV",
"cs.RO"
] | Tracking the 6DoF pose of unknown objects in monocular RGB video sequences is crucial for robotic manipulation. However, existing approaches typically rely on accurate depth information, which is non-trivial to obtain in real-world scenarios. Although depth estimation algorithms can be employed, geometric inaccuracy ca... | {
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2412.02270 | Sustainable Self-evolution Adversarial Training | [
"cs.CV",
"cs.AI"
] | With the wide application of deep neural network models in various computer vision tasks, there has been a proliferation of adversarial example generation strategies aimed at deeply exploring model security. However, existing adversarial training defense models, which rely on single or limited types of attacks under a ... | {
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2412.02271 | MediaSpin: Exploring Media Bias Through Fine-Grained Analysis of News
Headlines | [
"cs.CL"
] | In this paper, we introduce the MediaSpin dataset aiming to help in the development of models that can detect different forms of media bias present in news headlines, developed through human-supervised and -validated Large Language Model (LLM) labeling of media bias. This corpus comprises 78,910 pairs of news headlines... | {
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2412.02273 | Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World
Data and Deep Reinforcement Learning | [
"cs.LG"
] | Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis. Inspired by guidelines, we aim to develop a model that learns from electronic health records to determine the optimal sequence of actions for accurate diagnosis. Focusing on anemia and its sub-types, we employ deep reinforcement le... | {
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2412.02274 | Parallelizing the Computation of Robustness for Measuring the Strength
of Tuples | [
"cs.DB"
] | Several indicators have been recently proposed for measuring various characteristics of the tuples of a dataset -- particularly, the so-called skyline tuples, i.e., those that are not dominated by other tuples. Numeric indicators are very important as they may, e.g., provide an additional criterion to be used to rank s... | {
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2412.02275 | PCIM: Learning Pixel Attributions via Pixel-wise Channel Isolation
Mixing in High Content Imaging | [
"cs.CV"
] | Deep Neural Networks (DNNs) have shown remarkable success in various computer vision tasks. However, their black-box nature often leads to difficulty in interpreting their decisions, creating an unfilled need for methods to explain the decisions, and ultimately forming a barrier to their wide acceptance especially in b... | {
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2412.02279 | A Comprehensive Evaluation of Large Language Models on Aspect-Based
Sentiment Analysis | [
"cs.CL",
"cs.AI"
] | Recently, Large Language Models (LLMs) have garnered increasing attention in the field of natural language processing, revolutionizing numerous downstream tasks with powerful reasoning and generation abilities. For example, In-Context Learning (ICL) introduces a fine-tuning-free paradigm, allowing out-of-the-box LLMs t... | {
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2412.02280 | AH-OCDA: Amplitude-based Curriculum Learning and Hopfield Segmentation
Model for Open Compound Domain Adaptation | [
"cs.AI",
"cs.CV"
] | Open compound domain adaptation (OCDA) is a practical domain adaptation problem that consists of a source domain, target compound domain, and unseen open domain. In this problem, the absence of domain labels and pixel-level segmentation labels for both compound and open domains poses challenges to the direct applicatio... | {
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2412.02282 | Exploring Evolutionary Spectral Clustering for Temporal-Smoothed
Clustered Cell-Free Networking | [
"cs.NI",
"cs.IT",
"eess.SP",
"math.IT"
] | Clustered cell-free networking, which dynamically partitions the whole network into nonoverlapping subnetworks, has been recently proposed to mitigate the cell-edge problem in cellular networks. However, prior works only focused on optimizing clustered cell-free networking in static scenarios with fixed users. This cou... | {
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2412.02283 | VR Based Emotion Recognition Using Deep Multimodal Fusion With
Biosignals Across Multiple Anatomical Domains | [
"eess.SP",
"cs.AI"
] | Emotion recognition is significantly enhanced by integrating multimodal biosignals and IMU data from multiple domains. In this paper, we introduce a novel multi-scale attention-based LSTM architecture, combined with Squeeze-and-Excitation (SE) blocks, by leveraging multi-domain signals from the head (Meta Quest Pro VR ... | {
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2412.02285 | GQWformer: A Quantum-based Transformer for Graph Representation Learning | [
"cs.LG",
"cs.AI"
] | Graph Transformers (GTs) have demonstrated significant advantages in graph representation learning through their global attention mechanisms. However, the self-attention mechanism in GTs tends to neglect the inductive biases inherent in graph structures, making it chanllenging to effectively capture essential structura... | {
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2412.02287 | Viewpoint Consistency in 3D Generation via Attention and CLIP Guidance | [
"cs.CV"
] | Despite recent advances in text-to-3D generation techniques, current methods often suffer from geometric inconsistencies, commonly referred to as the Janus Problem. This paper identifies the root cause of the Janus Problem: viewpoint generation bias in diffusion models, which creates a significant gap between the actua... | {
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2412.02289 | Learn More by Using Less: Distributed Learning with Energy-Constrained
Devices | [
"cs.LG",
"cs.DC",
"eess.SP"
] | Federated Learning (FL) has emerged as a solution for distributed model training across decentralized, privacy-preserving devices, but the different energy capacities of participating devices (system heterogeneity) constrain real-world implementations. These energy limitations not only reduce model accuracy but also in... | {
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2412.02290 | Characterizing Information Shared by Participants to Coding Challenges:
The Case of Advent of Code | [
"cs.SI",
"cs.CL",
"cs.IR"
] | Advent of Code (AoC from now on) is a popular coding challenge requiring to solve programming puzzles for a variety of skill sets and levels. AoC follows the advent calendar, therefore it is an annual challenge that lasts for 25 days. AoC participants usually post their solutions on social networks and discuss them onl... | {
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2412.02291 | Conformal Symplectic Optimization for Stable Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Training deep reinforcement learning (RL) agents necessitates overcoming the highly unstable nonconvex stochastic optimization inherent in the trial-and-error mechanism. To tackle this challenge, we propose a physics-inspired optimization algorithm called relativistic adaptive gradient descent (RAD), which enhances lon... | {
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2412.02292 | Deep Matrix Factorization with Adaptive Weights for Multi-View
Clustering | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Recently, deep matrix factorization has been established as a powerful model for unsupervised tasks, achieving promising results, especially for multi-view clustering. However, existing methods often lack effective feature selection mechanisms and rely on empirical hyperparameter selection. To address these issues, we ... | {
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2412.02294 | Initial Study On Improving Segmentation By Combining Preoperative CT And
Intraoperative CBCT Using Synthetic Data | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Computer-Assisted Interventions enable clinicians to perform precise, minimally invasive procedures, often relying on advanced imaging methods. Cone-beam computed tomography (CBCT) can be used to facilitate computer-assisted interventions, despite often suffering from artifacts that pose challenges for accurate interpr... | {
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2412.02295 | CADMR: Cross-Attention and Disentangled Learning for Multimodal
Recommender Systems | [
"cs.IR",
"cs.AI",
"cs.LG"
] | The increasing availability and diversity of multimodal data in recommender systems offer new avenues for enhancing recommendation accuracy and user satisfaction. However, these systems must contend with high-dimensional, sparse user-item rating matrices, where reconstructing the matrix with only small subsets of prefe... | {
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2412.02301 | Large Multimodal Agents for Accurate Phishing Detection with Enhanced
Token Optimization and Cost Reduction | [
"cs.AI",
"cs.CL",
"cs.CR"
] | With the rise of sophisticated phishing attacks, there is a growing need for effective and economical detection solutions. This paper explores the use of large multimodal agents, specifically Gemini 1.5 Flash and GPT-4o mini, to analyze both URLs and webpage screenshots via APIs, thus avoiding the complexities of train... | {
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2412.02302 | Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based
Model Integrating Temporal and Covariate Interactions | [
"cs.LG",
"cs.AI"
] | Accurate photovoltaic (PV) power forecasting is critical for integrating renewable energy sources into the grid, optimizing real-time energy management, and ensuring energy reliability amidst increasing demand. However, existing models often struggle with effectively capturing the complex relationships between target v... | {
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2412.02306 | Partial Non-rigid Deformations and interpolations of Human Body Surfaces | [
"cs.CV"
] | Non-rigid shape deformations pose significant challenges, and most existing methods struggle to handle partial deformations effectively. We present Partial Non-rigid Deformations and interpolations of the human body Surfaces (PaNDAS), a new method to learn local and global deformations of 3D surface meshes by building ... | {
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2412.02308 | Bidding in Ancillary Service Markets: An Analytical Approach Using
Extreme Value Theory | [
"eess.SY",
"cs.SY"
] | To encourage the participation of stochastic distributed energy resources in Nordic ancillary service markets, the Danish transmission system operator, Energinet, has introduced grid codes requiring a minimum 90% reliability for the full availability of reserve capacity bids. This paper addresses the bidding strategy o... | {
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2412.02309 | An enhanced single Gaussian point continuum finite element formulation
using automatic differentiation | [
"cs.CE"
] | This contribution presents an improved low-order 3D finite element formulation with hourglass stabilization using automatic differentiation (AD). Here, the former Q1STc formulation is enhanced by an approximation-free computation of the inverse Jacobian. To this end, AD tools automate the computation and allow a direct... | {
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2412.02310 | Active Learning via Classifier Impact and Greedy Selection for
Interactive Image Retrieval | [
"cs.CV",
"cs.IR"
] | Active Learning (AL) is a user-interactive approach aimed at reducing annotation costs by selecting the most crucial examples to label. Although AL has been extensively studied for image classification tasks, the specific scenario of interactive image retrieval has received relatively little attention. This scenario pr... | {
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2412.02313 | Noisy Ostracods: A Fine-Grained, Imbalanced Real-World Dataset for
Benchmarking Robust Machine Learning and Label Correction Methods | [
"cs.LG",
"cs.CV"
] | We present the Noisy Ostracods, a noisy dataset for genus and species classification of crustacean ostracods with specialists' annotations. Over the 71466 specimens collected, 5.58% of them are estimated to be noisy (possibly problematic) at genus level. The dataset is created to addressing a real-world challenge: crea... | {
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} |
2412.02314 | Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised
Endoscopic Image Segmentation | [
"cs.CV"
] | The segmentation of endoscopic images plays a vital role in computer-aided diagnosis and treatment. The advancements in deep learning have led to the employment of numerous models for endoscopic tumor segmentation, achieving promising segmentation performance. Despite recent advancements, precise segmentation remains c... | {
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} |
2412.02315 | Topology Reconstruction of a Resistor Network with Limited Boundary
Measurements: An Optimization Approach | [
"eess.SY",
"cs.SY"
] | A problem of reconstruction of the topology and the respective edge resistance values of an unknown circular planar passive resistive network using limitedly available resistance distance measurements is considered. We develop a multistage topology reconstruction method, assuming that the number of boundary and interio... | {
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} |
2412.02316 | Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous
Autonomous Surface Vehicles with Deep Reinforcement Learning | [
"cs.RO",
"cs.LG"
] | This paper presents a model-free deep reinforcement learning framework for informative path planning with heterogeneous fleets of autonomous surface vehicles to locate and collect plastic waste. The system employs two teams of vehicles: scouts and cleaners. Coordination between these teams is achieved through a deep re... | {
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} |
2412.02317 | HumanRig: Learning Automatic Rigging for Humanoid Character in a Large
Scale Dataset | [
"cs.CV"
] | With the rapid evolution of 3D generation algorithms, the cost of producing 3D humanoid character models has plummeted, yet the field is impeded by the lack of a comprehensive dataset for automatic rigging, which is a pivotal step in character animation. Addressing this gap, we present HumanRig, the first large-scale d... | {
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} |
2412.02318 | Design of thermal meta-structures made of functionally graded materials
using isogeometric density-based topology optimization | [
"cs.CE"
] | The thermal conductivity of Functionally Graded Materials (FGMs) can be efficiently designed through topology optimization to obtain thermal meta-structures that actively steer the heat flow. Compared to conventional analytical design methods, topology optimization allows handling arbitrary geometries, boundary conditi... | {
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} |
2412.02322 | Controlling the Latent Diffusion Model for Generative Image Shadow
Removal via Residual Generation | [
"cs.CV"
] | Large-scale generative models have achieved remarkable advancements in various visual tasks, yet their application to shadow removal in images remains challenging. These models often generate diverse, realistic details without adequate focus on fidelity, failing to meet the crucial requirements of shadow removal, which... | {
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} |
2412.02323 | Pay Attention to the Robustness of Chinese Minority Language Models!
Syllable-level Textual Adversarial Attack on Tibetan Script | [
"cs.CL",
"cs.CR"
] | The textual adversarial attack refers to an attack method in which the attacker adds imperceptible perturbations to the original texts by elaborate design so that the NLP (natural language processing) model produces false judgments. This method is also used to evaluate the robustness of NLP models. Currently, most of t... | {
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} |
2412.02327 | Switchable deep beamformer for high-quality and real-time passive
acoustic mapping | [
"cs.AI",
"cs.LG",
"cs.SD",
"eess.AS"
] | Passive acoustic mapping (PAM) is a promising tool for monitoring acoustic cavitation activities in the applications of ultrasound therapy. Data-adaptive beamformers for PAM have better image quality compared to the time exposure acoustics (TEA) algorithms. However, the computational cost of data-adaptive beamformers i... | {
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} |
2412.02328 | Efficient Model Compression Techniques with FishLeg | [
"cs.LG"
] | In many domains, the most successful AI models tend to be the largest, indeed often too large to be handled by AI players with limited computational resources. To mitigate this, a number of compression methods have been developed, including methods that prune the network down to high sparsity whilst retaining performan... | {
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"cs.SY": 0
} |
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