Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
1806.01003	Distributed Learning from Interactions in Social Networks	We consider a network scenario in which agents can evaluate each other according to a score graph that models some interactions. The goal is to design a distributed protocol, run by the agents, that allows them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	99,460
2302.00370	How to select predictive models for causal inference?	As predictive models -- e.g., from machine learning -- give likely outcomes, they may be used to reason on the effect of an intervention, a causal-inference task. The increasing complexity of health data has opened the door to a plethora of models, but also the Pandora box of model selection: which of these models yiel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	343,195
1910.11615	A Multi-Phase Gammatone Filterbank for Speech Separation via TasNet	In this work, we investigate if the learned encoder of the end-to-end convolutional time domain audio separation network (Conv-TasNet) is the key to its recent success, or if the encoder can just as well be replaced by a deterministic hand-crafted filterbank. Motivated by the resemblance of the trained encoder of Conv-...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	150,839
2008.05657	Sparse Coding Driven Deep Decision Tree Ensembles for Nuclear Segmentation in Digital Pathology Images	In this paper, we propose an easily trained yet powerful representation learning approach with performance highly competitive to deep neural networks in a digital pathology image segmentation task. The method, called sparse coding driven deep decision tree ensembles that we abbreviate as ScD2TE, provides a new perspect...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	191,570
1608.05176	Multi-Operator Spectrum Sharing for Small Cell Networks : A Matching Game Perspective	One of the many problems faced by current cellular network technology is the under utilization of the dedicated, licensed spectrum of network operators. An emerging paradigm to solve this issue is to allow multiple operators to share some parts of each others' spectrum. Previous works on spectrum sharing have failed to...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	59,939
2101.00935	First-Order Methods for Convex Optimization	First-order methods for solving convex optimization problems have been at the forefront of mathematical optimization in the last 20 years. The rapid development of this important class of algorithms is motivated by the success stories reported in various applications, including most importantly machine learning, signal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,240
2301.03913	Measuring Board Game Distance	This paper presents a general approach for measuring distances between board games within the Ludii general game system. These distances are calculated using a previously published set of general board game concepts, each of which represents a common game idea or shared property. Our results compare and contrast two di...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	339,916
1708.06969	Hierarchical benchmark graphs for testing community detection algorithms	Hierarchical organization is an important, prevalent characteristic of complex systems; in order to understand their organization, the study of the underlying (generally complex) networks that describe the interactions between their constituents plays a central role. Numerous previous works have shown that many real-wo...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	79,410
1201.1623	MultiDendrograms: Variable-Group Agglomerative Hierarchical Clusterings	MultiDendrograms is a Java-written application that computes agglomerative hierarchical clusterings of data. Starting from a distances (or weights) matrix, MultiDendrograms is able to calculate its dendrograms using the most common agglomerative hierarchical clustering methods. The application implements a variable-gro...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	13,723
2405.01040	Few Shot Class Incremental Learning using Vision-Language models	Recent advancements in deep learning have demonstrated remarkable performance comparable to human capabilities across various supervised computer vision tasks. However, the prevalent assumption of having an extensive pool of training data encompassing all classes prior to model training often diverges from real-world s...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	451,194
1101.2524	Generalized Silver Codes	For an $n_t$ transmit, $n_r$ receive antenna system ($n_t \times n_r$ system), a {\it{full-rate}} space time block code (STBC) transmits $n_{min} = min(n_t,n_r)$ complex symbols per channel use. The well known Golden code is an example of a full-rate, full-diversity STBC for 2 transmit antennas. Its ML-decoding complex...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,809
2203.10093	Deep reinforcement learning guided graph neural networks for brain network analysis	Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and dise...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	286,397
2007.12497	Advanced Mapping Robot and High-Resolution Dataset	This paper presents a fully hardware synchronized mapping robot with support for a hardware synchronized external tracking system, for super-precise timing and localization. Nine high-resolution cameras and two 32-beam 3D Lidars were used along with a professional, static 3D scanner for ground truth map collection. Wit...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	188,835
1909.13330	Neural Hybrid Recommender: Recommendation needs collaboration	In recent years, deep learning has gained an indisputable success in computer vision, speech recognition, and natural language processing. After its rising success on these challenging areas, it has been studied on recommender systems as well, but mostly to include content features into traditional methods. In this pap...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	147,386
2307.10281	Semi-supervised Cycle-GAN for face photo-sketch translation in the wild	The performance of face photo-sketch translation has improved a lot thanks to deep neural networks. GAN based methods trained on paired images can produce high-quality results under laboratory settings. Such paired datasets are, however, often very small and lack diversity. Meanwhile, Cycle-GANs trained with unpaired p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	380,505
2010.05961	Perceptimatic: A human speech perception benchmark for unsupervised subword modelling	In this paper, we present a data set and methods to compare speech processing models and human behaviour on a phone discrimination task. We provide Perceptimatic, an open data set which consists of French and English speech stimuli, as well as the results of 91 English- and 93 French-speaking listeners. The stimuli tes...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	200,309
1704.06877	Learning to Skim Text	Recurrent Neural Networks are showing much promise in many sub-areas of natural language processing, ranging from document classification to machine translation to automatic question answering. Despite their promise, many recurrent models have to read the whole text word by word, making it slow to handle long documents...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	72,244
2310.13767	Graph AI in Medicine	In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks (GNNs), stands out for its capability to capture intricate relationships within structured clinical datasets. With diverse data -- from patient records to imaging -- GNNs process data holistically by viewing mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	401,565
2403.12017	Supervised Fine-Tuning as Inverse Reinforcement Learning	The prevailing approach to aligning Large Language Models (LLMs) typically relies on human or AI feedback and assumes access to specific types of preference datasets. In our work, we question the efficacy of such datasets and explore various scenarios where alignment with expert demonstrations proves more realistic. We...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	438,980
2406.15811	PointDreamer: Zero-shot 3D Textured Mesh Reconstruction from Colored Point Cloud	Reconstructing textured meshes from colored point clouds is an important but challenging task. Most existing methods yield blurry-looking textures or rely on 3D training data that are hard to acquire. Regarding this, we propose PointDreamer, a novel framework for textured mesh reconstruction from colored point cloud vi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	466,878
2306.02873	DecompX: Explaining Transformers Decisions by Propagating Token Decomposition	An emerging solution for explaining Transformer-based models is to use vector-based analysis on how the representations are formed. However, providing a faithful vector-based explanation for a multi-layer model could be challenging in three aspects: (1) Incorporating all components into the analysis, (2) Aggregating th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	371,095
1908.08307	Image Colorization By Capsule Networks	In this paper, a simple topology of Capsule Network (CapsNet) is investigated for the problem of image colorization. The generative and segmentation capabilities of the original CapsNet topology, which is proposed for image classification problem, is leveraged for the colorization of the images by modifying the network...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	142,516
2111.13998	Targeted Supervised Contrastive Learning for Long-Tailed Recognition	Real-world data often exhibits long tail distributions with heavy class imbalance, where the majority classes can dominate the training process and alter the decision boundaries of the minority classes. Recently, researchers have investigated the potential of supervised contrastive learning for long-tailed recognition,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,457
2110.08693	Elastic Shape Analysis of Tree-like 3D Objects using Extended SRVF Representation	How can one analyze detailed 3D biological objects, such as neurons and botanical trees, that exhibit complex geometrical and topological variation? In this paper, we develop a novel mathematical framework for representing, comparing, and computing geodesic deformations between the shapes of such tree-like 3D objects. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	261,514
2211.17142	Learning Label Modular Prompts for Text Classification in the Wild	Machine learning models usually assume i.i.d data during training and testing, but data and tasks in real world often change over time. To emulate the transient nature of real world, we propose a challenging but practical task: text classification in-the-wild, which introduces different non-stationary training/testing ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	333,876
2311.07912	Detection of Small Targets in Sea Clutter Based on RepVGG and Continuous Wavelet Transform	Constructing a high-performance target detector under the background of sea clutter is always necessary and important. In this work, we propose a RepVGGA0-CWT detector, where RepVGG is a residual network that gains a high detection accuracy. Different from traditional residual networks, RepVGG keeps an acceptable calcu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	407,515
2406.08506	RGFN: Synthesizable Molecular Generation Using GFlowNets	Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making exper...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	463,519
2402.17018	A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection	We tested front-end enhanced neural models where a frozen classifier was prepended by a differentiable and fully convolutional model with a skip connection. By training them using a small learning rate for about one epoch, we obtained models that retained the accuracy of the backbone classifier while being unusually re...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	432,808
1008.3305	Celer: an Efficient Program for Genotype Elimination	This paper presents an efficient program for checking Mendelian consistency in a pedigree. Since pedigrees may contain incomplete and/or erroneous information, geneticists need to pre-process them before performing linkage analysis. Removing superfluous genotypes that do not respect the Mendelian inheritance laws can s...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	7,311
2412.03903	Using SlowFast Networks for Near-Miss Incident Analysis in Dashcam Videos	This paper classifies near-miss traffic videos using the SlowFast deep neural network that mimics the characteristics of the slow and fast visual information processed by two different streams from the M (Magnocellular) and P (Parvocellular) cells of the human brain. The approach significantly improves the accuracy of ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	514,161
2404.02677	The VoicePrivacy 2024 Challenge Evaluation Plan	The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional states. The organizers provide development and evaluation datasets and evaluation scripts, as well as baseline anonymization systems and a li...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	443,961
2310.00492	From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning	Large Language Models (LLMs) have achieved remarkable success, where instruction tuning is the critical step in aligning LLMs with user intentions. In this work, we investigate how the instruction tuning adjusts pre-trained models with a focus on intrinsic changes. Specifically, we first develop several local and globa...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	396,003
2412.18022	Trustworthy and Efficient LLMs Meet Databases	In the rapidly evolving AI era with large language models (LLMs) at the core, making LLMs more trustworthy and efficient, especially in output generation (inference), has gained significant attention. This is to reduce plausible but faulty LLM outputs (a.k.a hallucinations) and meet the highly increased inference deman...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	520,213
2004.06866	On the Linguistic Capacity of Real-Time Counter Automata	Counter machines have achieved a newfound relevance to the field of natural language processing (NLP): recent work suggests some strong-performing recurrent neural networks utilize their memory as counters. Thus, one potential way to understand the success of these networks is to revisit the theory of counter computati...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	172,624
2308.06983	pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems	Nearest neighbor (NN) sampling provides more semantic variations than pre-defined transformations for self-supervised learning (SSL) based image recognition problems. However, its performance is restricted by the quality of the support set, which holds positive samples for the contrastive loss. In this work, we show th...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	385,354
2302.13567	Towards Audit Requirements for AI-based Systems in Mobility Applications	Various mobility applications like advanced driver assistance systems increasingly utilize artificial intelligence (AI) based functionalities. Typically, deep neural networks (DNNs) are used as these provide the best performance on the challenging perception, prediction or planning tasks that occur in real driving envi...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	347,995
2303.02885	Improving Transformer-based Image Matching by Cascaded Capturing Spatially Informative Keypoints	Learning robust local image feature matching is a fundamental low-level vision task, which has been widely explored in the past few years. Recently, detector-free local feature matchers based on transformers have shown promising results, which largely outperform pure Convolutional Neural Network (CNN) based ones. But c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	349,524
2312.07504	COLMAP-Free 3D Gaussian Splatting	While neural rendering has led to impressive advances in scene reconstruction and novel view synthesis, it relies heavily on accurately pre-computed camera poses. To relax this constraint, multiple efforts have been made to train Neural Radiance Fields (NeRFs) without pre-processed camera poses. However, the implicit r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,944
1810.12832	General audio tagging with ensembling convolutional neural network and statistical features	Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solution for the DCASE 2018 Task 2 general audio tagging challenge. The contributions of our solution include: We investigated a variety of convo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,857
2412.00435	Benchmark Real-time Adaptation and Communication Capabilities of Embodied Agent in Collaborative Scenarios	Advancements in Large Language Models (LLMs) have opened transformative possibilities for human-robot interaction, especially in collaborative environments. However, Real-time human-AI collaboration requires agents to adapt to unseen human behaviors while maintaining effective communication dynamically. Existing benchm...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	512,653
2203.05625	PETR: Position Embedding Transformation for Multi-View 3D Object Detection	In this paper, we develop position embedding transformation (PETR) for multi-view 3D object detection. PETR encodes the position information of 3D coordinates into image features, producing the 3D position-aware features. Object query can perceive the 3D position-aware features and perform end-to-end object detection. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	284,856
2403.10357	ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image	Recent progress in human shape learning, shows that neural implicit models are effective in generating 3D human surfaces from limited number of views, and even from a single RGB image. However, existing monocular approaches still struggle to recover fine geometric details such as face, hands or cloth wrinkles. They are...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	438,163
2012.06008	Price Suggestion for Online Second-hand Items with Texts and Images	This paper presents an intelligent price suggestion system for online second-hand listings based on their uploaded images and text descriptions. The goal of price prediction is to help sellers set effective and reasonable prices for their second-hand items with the images and text descriptions uploaded to the online pl...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	210,956
2303.14369	Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning	Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs. To clarify this coarse-grained global interaction and move a step further, we have to encounter challenging shell-breaking...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	354,056
2104.04107	FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems	We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: the nature of perturbation (e.g., pixel-level or face accessories), the attacker's system knowledge (about training data and learning archite...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	229,288
2106.14487	A Meta-Heuristic Search Algorithm based on Infrasonic Mating Displays in Peafowls	Meta-heuristic techniques are important as they are used to find solutions to computationally intractable problems. Simplistic methods such as exhaustive search become computationally expensive and unreliable as the solution space for search algorithms increase. As no method is guaranteed to perform better than all oth...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	243,433
1301.1609	Two Design Issues in Cognitive Sub-Small Cell for Sojourners	In this paper, we propound a solution named Cognitive Sub-Small Cell for Sojourners (CSCS) in allusion to a broadly representative small cell scenario, where users can be categorized into two groups: sojourners and inhabitants. CSCS contributes to save energy, enhance the number of concurrently supportable users and en...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,871
2104.12225	DC3: A learning method for optimization with hard constraints	Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the use of deep networks as cheap "approximate solvers." Unfortunately, naive deep learning approaches typically cannot enforce the hard constraints of such problems, leading to infea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	232,146
2407.05591	On the Power of Convolution Augmented Transformer	The transformer architecture has catalyzed revolutionary advances in language modeling. However, recent architectural recipes, such as state-space models, have bridged the performance gap. Motivated by this, we examine the benefits of Convolution-Augmented Transformer (CAT) for recall, copying, and length generalizatio...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	471,042
2404.18514	A Systematic Evaluation of Adversarial Attacks against Speech Emotion Recognition Models	Speech emotion recognition (SER) is constantly gaining attention in recent years due to its potential applications in diverse fields and thanks to the possibility offered by deep learning technologies. However, recent studies have shown that deep learning models can be vulnerable to adversarial attacks. In this paper, ...	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	450,288
1911.06573	Independent and automatic evaluation of acoustic-to-articulatory inversion models	Reconstruction of articulatory trajectories from the acoustic speech signal has been proposed for improving speech recognition and text-to-speech synthesis. However, to be useful in these settings, articulatory reconstruction must be speaker independent. Furthermore, as most research focuses on single, small datasets w...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	153,576
1904.08528	Robust Exploration with Tight Bayesian Plausibility Sets	Optimism about the poorly understood states and actions is the main driving force of exploration for many provably-efficient reinforcement learning algorithms. We propose optimism in the face of sensible value functions (OFVF)- a novel data-driven Bayesian algorithm to constructing Plausibility sets for MDPs to explore...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	128,099
1312.5857	A Generative Product-of-Filters Model of Audio	We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain. PoF makes similar assumptions to those used in the classic homomorphic filtering approach to signal processing, but replaces hand-designed decompositions...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	29,276
2207.01806	Aesthetic Attribute Assessment of Images Numerically on Mixed Multi-attribute Datasets	With the continuous development of social software and multimedia technology, images have become a kind of important carrier for spreading information and socializing. How to evaluate an image comprehensively has become the focus of recent researches. The traditional image aesthetic assessment methods often adopt singl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	306,302
1812.01710	GANtruth - an unpaired image-to-image translation method for driving scenarios	Synthetic image translation has significant potentials in autonomous transportation systems. That is due to the expense of data collection and annotation as well as the unmanageable diversity of real-words situations. The main issue with unpaired image-to-image translation is the ill-posed nature of the problem. In thi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	115,584
1812.08468	One-Class Feature Learning Using Intra-Class Splitting	This paper proposes a novel generic one-class feature learning method based on intra-class splitting. In one-class classification, feature learning is challenging, because only samples of one class are available during training. Hence, state-of-the-art methods require reference multi-class datasets to pretrain feature ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	117,006
2411.11196	PickScan: Object discovery and reconstruction from handheld interactions	Reconstructing compositional 3D representations of scenes, where each object is represented with its own 3D model, is a highly desirable capability in robotics and augmented reality. However, most existing methods rely heavily on strong appearance priors for object discovery, therefore only working on those classes of ...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	true	508,950
2009.01174	Transform Quantization for CNN (Convolutional Neural Network) Compression	In this paper, we compress convolutional neural network (CNN) weights post-training via transform quantization. Previous CNN quantization techniques tend to ignore the joint statistics of weights and activations, producing sub-optimal CNN performance at a given quantization bit-rate, or consider their joint statistics ...	false	false	false	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	194,236
2411.14961	LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement	Foundation models (FMs) achieve strong performance across diverse tasks with task-specific fine-tuning, yet full parameter fine-tuning is often computationally prohibitive for large models. Parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaptation (LoRA) reduce this cost by introducing low-rank matrices f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	510,385
2204.11251	One-Shot Domain-Adaptive Imitation Learning via Progressive Learning	Traditional deep learning-based visual imitation learning techniques require a large amount of demonstration data for model training, and the pre-trained models are difficult to adapt to new scenarios. To address these limitations, we propose a unified framework using a novel progressive learning approach comprised of ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	293,086
2305.04561	Boosting Radiology Report Generation by Infusing Comparison Prior	Recent transformer-based models have made significant strides in generating radiology reports from chest X-ray images. However, a prominent challenge remains: these models often lack prior knowledge, resulting in the generation of synthetic reports that mistakenly reference non-existent prior exams. This discrepancy ca...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	362,821
2303.04361	Sample Efficient Multimodal Semantic Augmentation for Incremental Summarization	In this work, we develop a prompting approach for incremental summarization of task videos. We develop a sample-efficient few-shot approach for extracting semantic concepts as an intermediate step. We leverage an existing model for extracting the concepts from the images and extend it to videos and introduce a clusteri...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	350,060
2311.13539	Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression	We study 3D point cloud attribute compression via a volumetric approach: assuming point cloud geometry is known at both encoder and decoder, parameters $\theta$ of a continuous attribute function $f: \mathbb{R}^3 \mapsto \mathbb{R}$ are quantized to $\hat{\theta}$ and encoded, so that discrete samples $f_{\hat{\theta}}...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	409,766
1806.00388	A Review of Challenges and Opportunities in Machine Learning for Health	Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	99,298
2102.03521	Haptic-enabled Mixed Reality System for Mixed-initiative Remote Robot Control	Robots assist in many areas that are considered unsafe for humans to operate. For instance, in handling pandemic diseases such as the recent Covid-19 outbreak and other outbreaks like Ebola, robots can assist in reaching areas dangerous for humans and do simple tasks such as pick up the correct medicine (among a set of...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	218,777
2009.11436	Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning	The system we used for Task 6 (Automated Audio Captioning)of the Detection and Classification of Acoustic Scenes and Events(DCASE) 2020 Challenge combines three elements, namely, dataaugmentation, multi-task learning, and post-processing, for audiocaptioning. The system received the highest evaluation scores, butwhich ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	197,169
1806.00553	Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems	Traditional exploration methods in RL require agents to perform random actions to find rewards. But these approaches struggle on sparse-reward domains like Montezuma's Revenge where the probability that any random action sequence leads to reward is extremely low. Recent algorithms have performed well on such tasks by e...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	99,330
1803.00182	SIR Meta Distribution of K-Tier Downlink Heterogeneous Cellular Networks with Cell Range Expansion	Heterogeneous cellular networks (HCNs) constitute a necessary step in the evolution of cellular networks. In this paper, we apply the signal-to-interference ratio (SIR) meta distribution framework for a refined SIR performance analysis of HCNs, focusing on K-tier heterogeneous cellular networks based on the homogeneous...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	91,613
2406.09867	Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox	Most existing out-of-distribution (OOD) detection benchmarks classify samples with novel labels as the OOD data. However, some marginal OOD samples actually have close semantic contents to the in-distribution (ID) sample, which makes determining the OOD sample a Sorites Paradox. In this paper, we construct a benchmark ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	464,120
2403.00961	Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice	It is becoming increasingly important that physics educators equip their students with the skills to work with data effectively. However, many educators may lack the necessary training and expertise in data science to teach these skills. To address this gap, we created the Data Science Education Community of Practice (...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	434,199
2406.12050	Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning	Supervised fine-tuning enhances the problem-solving abilities of language models across various mathematical reasoning tasks. To maximize such benefits, existing research focuses on broadening the training set with various data augmentation techniques, which is effective for standard single-round question-answering set...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	465,199
1510.01363	Cooperative spectrum sensing schemes with partial statistics knowledge	In this letter, we analyze the problem of detecting spectrum holes in cognitive radio systems. We consider that a group of unlicensed users can sense the radio signal energy, perform some simple processing and transmit the result to a central entity, where the decision about the presence or not of licensed users is mad...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	47,605
1508.02570	A Combinatorial Model of Interference in Frequency Hopping Schemes	In a frequency hopping (FH) scheme users communicate simultaneously using FH sequences defined on the same set of frequency channels. An FH sequence specifies the frequency channel to be used as communication progresses. Much of the research on the performance of FH schemes is based on either pairwise mutual interferen...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,919
2111.07897	On Sparse High-Dimensional Graphical Model Learning For Dependent Time Series	We consider the problem of inferring the conditional independence graph (CIG) of a sparse, high-dimensional stationary multivariate Gaussian time series. A sparse-group lasso-based frequency-domain formulation of the problem based on frequency-domain sufficient statistic for the observed time series is presented. We in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	266,506
2110.06390	Learning ground states of quantum Hamiltonians with graph networks	Solving for the lowest energy eigenstate of the many-body Schrodinger equation is a cornerstone problem that hinders understanding of a variety of quantum phenomena. The difficulty arises from the exponential nature of the Hilbert space which casts the governing equations as an eigenvalue problem of exponentially large...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	260,595
1906.04165	Leveraging BERT for Extractive Text Summarization on Lectures	In the last two decades, automatic extractive text summarization on lectures has demonstrated to be a useful tool for collecting key phrases and sentences that best represent the content. However, many current approaches utilize dated approaches, producing sub-par outputs or requiring several hours of manual tuning to ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	134,626
2404.00597	Parameter and Data-Efficient Spectral StyleDCGAN	We present a simple, highly parameter, and data-efficient adversarial network for unconditional face generation. Our method: Spectral Style-DCGAN or SSD utilizes only 6.574 million parameters and 4739 dog faces from the Animal Faces HQ (AFHQ) dataset as training samples while preserving fidelity at low resolutions up t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,015
2001.11107	Hamiltonian neural networks for solving equations of motion	There has been a wave of interest in applying machine learning to study dynamical systems. We present a Hamiltonian neural network that solves the differential equations that govern dynamical systems. This is an equation-driven machine learning method where the optimization process of the network depends solely on the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	161,973
1110.2659	Efficient Detection of Hot Span in Information Diffusion from Observation	We addressed the problem of detecting the change in behavior of information diffusion from a small amount of observation data, where the behavior changes were assumed to be effectively reflected in changes in the diffusion parameter value. The problem is to detect where in time and how long this change persisted and ho...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,600
2410.14702	Polymath: A Challenging Multi-modal Mathematical Reasoning Benchmark	Multi-modal Large Language Models (MLLMs) exhibit impressive problem-solving abilities in various domains, but their visual comprehension and abstract reasoning skills remain under-evaluated. To this end, we present PolyMATH, a challenging benchmark aimed at evaluating the general cognitive reasoning abilities of MLLMs...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	500,156
0902.4577	Using Distributed Rate-Splitting Game to Approach Rate Region Boundary of the Gaussian Interference Channel	Determining how to approach the rate boundary of the Gaussian interference channel in practical system is a big concern. In this paper, a distributed rate-splitting (DRS) scheme is proposed to approach the rate region boundary of the Gaussian interference channel. It is shown that the DRS scheme can be formulated as a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,242
2203.01193	VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surface	In road monitoring, it is an important issue to detect changes in the road surface at an early stage to prevent damage to third parties. The target of the falling object may be a fallen tree due to the external force of a flood or an earthquake, and falling rocks from a slope. Generative deep learning is possible to fl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	283,287
2101.00629	A comparison of matrix-free isogeometric Galerkin and collocation methods for Karhunen--Lo\`eve expansion	Numerical computation of the Karhunen--Lo\`eve expansion is computationally challenging in terms of both memory requirements and computing time. We compare two state-of-the-art methods that claim to efficiently solve for the K--L expansion: (1) the matrix-free isogeometric Galerkin method using interpolation based quad...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	214,148
2407.16515	Spurious Correlations in Concept Drift: Can Explanatory Interaction Help?	Long-running machine learning models face the issue of concept drift (CD), whereby the data distribution changes over time, compromising prediction performance. Updating the model requires detecting drift by monitoring the data and/or the model for unexpected changes. We show that, however, spurious correlations (SCs) ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	475,627
2404.07072	Implicit Multi-Spectral Transformer: An Lightweight and Effective Visible to Infrared Image Translation Model	In the field of computer vision, visible light images often exhibit low contrast in low-light conditions, presenting a significant challenge. While infrared imagery provides a potential solution, its utilization entails high costs and practical limitations. Recent advancements in deep learning, particularly the deploym...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	445,706
2405.17272	DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems	The min-max vehicle routing problem (min-max VRP) traverses all given customers by assigning several routes and aims to minimize the length of the longest route. Recently, reinforcement learning (RL)-based sequential planning methods have exhibited advantages in solving efficiency and optimality. However, these methods...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	457,838
2203.02884	Towards Self-Supervised Category-Level Object Pose and Size Estimation	In this work, we tackle the challenging problem of category-level object pose and size estimation from a single depth image. Although previous fully-supervised works have demonstrated promising performance, collecting ground-truth pose labels is generally time-consuming and labor-intensive. Instead, we propose a label-...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	283,895
2109.06589	Information Cocoons in Online Navigation	Social media and online navigation bring us enjoyable experience in accessing information, and simultaneously create information cocoons (ICs) in which we are unconsciously trapped with limited and biased information. We provide a formal definition of IC in the scenario of online navigation. Subsequently, by analyzing ...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	255,206
1205.7025	Engineering hierarchical complex systems: an agent-based approach. The case of flexible manufacturing systems	This article introduces a formal model to specify, model and validate hierarchical complex systems described at different levels of analysis. It relies on concepts that have been developed in the multi-agent-based simulation (MABS) literature: level, influence and reaction. One application of such model is the specific...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	16,267
2308.15752	Large-scale data extraction from the UNOS organ donor documents	In this paper we focus on three major task: 1) discussing our methods: Our method captures a portion of the data in DCD flowsheets, kidney perfusion data, and Flowsheet data captured peri-organ recovery surgery. 2) demonstrating the result: We built a comprehensive, analyzable database from 2022 OPTN data. This dataset...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	388,780
2403.11482	SeisFusion: Constrained Diffusion Model with Input Guidance for 3D Seismic Data Interpolation and Reconstruction	Geographical, physical, or economic constraints often result in missing traces within seismic data, making the reconstruction of complete seismic data a crucial step in seismic data processing. Traditional methods for seismic data reconstruction require the selection of multiple empirical parameters and struggle to han...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	438,720
1705.02145	Part-based Deep Hashing for Large-scale Person Re-identification	Large-scale is a trend in person re-identification (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	72,938
1806.07956	Reconstructing networks with unknown and heterogeneous errors	The vast majority of network datasets contains errors and omissions, although this is rarely incorporated in traditional network analysis. Recently, an increasing effort has been made to fill this methodological gap by developing network reconstruction approaches based on Bayesian inference. These approaches, however, ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	101,055
1904.08034	People infer recursive visual concepts from just a few examples	Machine learning has made major advances in categorizing objects in images, yet the best algorithms miss important aspects of how people learn and think about categories. People can learn richer concepts from fewer examples, including causal models that explain how members of a category are formed. Here, we explore the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	127,947
2101.11440	Online Extrinsic Calibration based on Per-Sensor Ego-Motion Using Dual Quaternions	In this work, we propose an approach for extrinsic sensor calibration from per-sensor ego-motion estimates. Our problem formulation is based on dual quaternions, enabling two different online capable solving approaches. We provide a certifiable globally optimal and a fast local approach along with a method to verify th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	217,279
2109.07604	Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification	Traditional hand-crafted linguistically-informed features have often been used for distinguishing between translated and original non-translated texts. By contrast, to date, neural architectures without manual feature engineering have been less explored for this task. In this work, we (i) compare the traditional featur...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	255,583
1812.00123	Snapshot Distillation: Teacher-Student Optimization in One Generation	Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting. Teacher-student optimization aims at providing complementary cues from a model trained previously, but these approaches are often considerably slow due to the pipeline of training a few...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	115,165
2008.02274	Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM	Map-centric SLAM utilizes elasticity as a means of loop closure. This approach reduces the cost of loop closure while still provides large-scale fusion-based dense maps, when compared to the trajectory-centric SLAM approaches. In this paper, we present a novel framework for 3D LiDAR-based map-centric SLAM. Having the a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	190,575
2006.04260	Formal synthesis of closed-form sampled-data controllers for nonlinear continuous-time systems under STL specifications	We propose a counterexample-guided inductive synthesis framework for the formal synthesis of closed-form sampled-data controllers for nonlinear systems to meet STL specifications over finite-time trajectories. Rather than stating the STL specification for a single initial condition, we consider an (infinite and bounded...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	180,628
2211.09752	Learning to Counterfactually Explain Recommendations	Recommender system practitioners are facing increasing pressure to explain recommendations. We explore how to explain recommendations using counterfactual logic, i.e. "Had you not interacted with the following items, we would not recommend it." Compared to the traditional explanation logic, counterfactual explanations ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	331,084

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