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
2012.01447	Relevance in the Renormalization Group and in Information Theory	The analysis of complex physical systems hinges on the ability to extract the relevant degrees of freedom from among the many others. Though much hope is placed in machine learning, it also brings challenges, chief of which is interpretability. It is often unclear what relation, if any, the architecture- and training-d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	209,417
2205.11827	Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization	We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization. The framework unifies a tailored acquisition function, a parallel acquisition procedure, and the integration of process information providing context to the optimization pr...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	298,303
1605.02305	Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks	Depth estimation from single monocular images is a key component of scene understanding and has benefited largely from deep convolutional neural networks (CNN) recently. In this article, we take advantage of the recent deep residual networks and propose a simple yet effective approach to this problem. We formulate dept...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	55,612
2502.13675	A CFL condition for the finite cell method	Immersed boundary finite element methods allow the user to bypass the potentially troublesome task of boundary-conforming mesh generation. However, they suffer from the influence of cut elements, i.e., elements that are intersected by the physical domain boundaries. When combined with explicit time integration, poorly ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	535,464
2407.14394	TTT: A Temporal Refinement Heuristic for Tenuously Tractable Discrete Time Reachability Problems	Reachable set computation is an important tool for analyzing control systems. Simulating a control system can show that the system is generally functioning as desired, but a formal tool like reachability analysis can provide a guarantee of correctness. For linear systems, reachability analysis is straightforward and fa...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	true	474,768
2402.14268	Can Large Language Models Detect Misinformation in Scientific News Reporting?	Scientific facts are often spun in the popular press with the intent to influence public opinion and action, as was evidenced during the COVID-19 pandemic. Automatic detection of misinformation in the scientific domain is challenging because of the distinct styles of writing in these two media types and is still in its...	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	false	false	false	431,599
1805.11123	Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images	In this paper, we explore the problem of training one-look regression models for counting objects in datasets comprising a small number of high-resolution, variable-shaped images. We illustrate that conventional global average pooling (GAP) based models are unreliable due to the patchwise cancellation of true overestim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	98,843
1903.03189	Incorporating social practices in BDI agent systems	When agents interact with humans, either through embodied agents or because they are embedded in a robot, it would be easy if they could use fixed interaction protocols as they do with other agents. However, people do not keep fixed protocols in their day-to-day interactions and the environments are often dynamic, maki...	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	123,662
2403.11791	PAON: A New Neuron Model using Pad\'e Approximants	Convolutional neural networks (CNN) are built upon the classical McCulloch-Pitts neuron model, which is essentially a linear model, where the nonlinearity is provided by a separate activation function. Several researchers have proposed enhanced neuron models, including quadratic neurons, generalized operational neurons...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	438,863
1708.06673	Tags2Parts: Discovering Semantic Regions from Shape Tags	We propose a novel method for discovering shape regions that strongly correlate with user-prescribed tags. For example, given a collection of chairs tagged as either "has armrest" or "lacks armrest", our system correctly highlights the armrest regions as the main distinctive parts between the two chair types. To obtain...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	79,360
2405.11056	A Comparative Study of Garment Draping Techniques	We present a comparison review that evaluates popular techniques for garment draping for 3D fashion design, virtual try-ons, and animations. A comparative study is performed between various methods for garment draping of clothing over the human body. These include numerous models, such as physics and machine learning b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	454,988
2005.07173	Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI	We demonstrate a unified approach to rigorous design of safety-critical autonomous systems using the VerifAI toolkit for formal analysis of AI-based systems. VerifAI provides an integrated toolchain for tasks spanning the design process, including modeling, falsification, debugging, and ML component retraining. We eval...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	177,216
2001.01126	Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health Classification	Graph and language embedding models are becoming commonplace in large scale analyses given their ability to represent complex sparse data densely in low-dimensional space. Integrating these models' complementary relational and communicative data may be especially helpful if predicting rare events or classifying members...	false	false	false	true	true	false	true	false	true	false	false	false	false	false	false	false	false	false	159,413
1408.1479	Logarithmic-Time Updates and Queries in Probabilistic Networks	In this paper we propose a dynamic data structure that supports efficient algorithms for updating and querying singly connected Bayesian networks (causal trees and polytrees). In the conventional algorithms, new evidence in absorbed in time O(1) and queries are processed in time O(N), where N is the size of the network...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	35,169
2209.13808	Streaming Video Temporal Action Segmentation In Real Time	Temporal action segmentation (TAS) is a critical step toward long-term video understanding. Recent studies follow a pattern that builds models based on features instead of raw video picture information. However, we claim those models are trained complicatedly and limit application scenarios. It is hard for them to segm...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	320,032
2201.02435	Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning	Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	274,541
2103.11505	Policy-Guided Heuristic Search with Guarantees	The use of a policy and a heuristic function for guiding search can be quite effective in adversarial problems, as demonstrated by AlphaGo and its successors, which are based on the PUCT search algorithm. While PUCT can also be used to solve single-agent deterministic problems, it lacks guarantees on its search effort ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	225,825
1709.07192	Visual Question Generation as Dual Task of Visual Question Answering	Recently visual question answering (VQA) and visual question generation (VQG) are two trending topics in the computer vision, which have been explored separately. In this work, we propose an end-to-end unified framework, the Invertible Question Answering Network (iQAN), to leverage the complementary relations between q...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,238
1707.02279	A Probabilistic Calculus of Cyber-Physical Systems	We propose a hybrid probabilistic process calculus for modelling and reasoning on cyber-physical systems (CPSs). The dynamics of the calculus is expressed in terms of a probabilistic labelled transition system in the SOS style of Plotkin. This is used to define a bisimulation-based probabilistic behavioural semantics w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	76,675
1308.2428	Hidden Structure and Function in the Lexicon	How many words are needed to define all the words in a dictionary? Graph-theoretic analysis reveals that about 10% of a dictionary is a unique Kernel of words that define one another and all the rest, but this is not the smallest such subset. The Kernel consists of one huge strongly connected component (SCC), about hal...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	26,382
2210.07769	Flattened Graph Convolutional Networks For Recommendation	Graph Convolutional Networks (GCNs) and their variants have achieved significant performances on various recommendation tasks. However, many existing GCN models tend to perform recursive aggregations among all related nodes, which can arise severe computational burden to hinder their application to large-scale recommen...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	323,860
2312.11312	APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation	Automatic Post-Editing (APE) is the task of automatically identifying and correcting errors in the Machine Translation (MT) outputs. We propose a repair-filter-use methodology that uses an APE system to correct errors on the target side of the MT training data. We select the sentence pairs from the original and correct...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	416,512
1910.06827	Learning Generalisable Omni-Scale Representations for Person Re-Identification	An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation. In this paper, we develop novel CNN architectures to address both challenges. First,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	149,457
2402.05011	Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching	Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing a compact counterpart without sacrificing the performance of Graph Neural Networks (GNNs) trained on it, which has shed light on reducing the computational cost for training GNNs. Nevertheless, existing methods often fall short of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,686
2107.08957	Clinical Relation Extraction Using Transformer-based Models	The newly emerged transformer technology has a tremendous impact on NLP research. In the general English domain, transformer-based models have achieved state-of-the-art performances on various NLP benchmarks. In the clinical domain, researchers also have investigated transformer models for clinical applications. The go...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	246,889
1412.4538	Towards Error Handling in a DSL for Robot Assembly Tasks	This work-in-progress paper presents our work with a domain specific language (DSL) for tackling the issue of programming robots for small-sized batch production. We observe that as the complexity of assembly increases so does the likelihood of errors, and these errors need to be addressed. Nevertheless, it is essentia...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	38,409
1804.05370	A Sparse Non-negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior from MRI	Muscle coordination patterns of lingual behaviors are synergies generated by deforming local muscle groups in a variety of ways. Functional units are functional muscle groups of local structural elements within the tongue that compress, expand, and move in a cohesive and consistent manner. Identifying the functional un...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	95,057
2010.11472	An explainable deep vision system for animal classification and detection in trail-camera images with automatic post-deployment retraining	This paper introduces an automated vision system for animal detection in trail-camera images taken from a field under the administration of the Texas Parks and Wildlife Department. As traditional wildlife counting techniques are intrusive and labor intensive to conduct, trail-camera imaging is a comparatively non-intru...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	202,268
2109.12300	Finetuning Transformer Models to Build ASAG System	Research towards creating systems for automatic grading of student answers to quiz and exam questions in educational settings has been ongoing since 1966. Over the years, the problem was divided into many categories. Among them, grading text answers were divided into short answer grading, and essay grading. The goal of...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	257,231
2412.00020	Partitioning Message Passing for Graph Fraud Detection	Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks. Existing GNN-based GFD models are designed to augment graph structure to accommodate the inductive bias of GNNs towards homophily, by excluding hete...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	512,445
2009.01195	Garain at SemEval-2020 Task 12: Sequence based Deep Learning for Categorizing Offensive Language in Social Media	SemEval-2020 Task 12 was OffenseEval: Multilingual Offensive Language Identification in Social Media (Zampieri et al., 2020). The task was subdivided into multiple languages and datasets were provided for each one. The task was further divided into three sub-tasks: offensive language identification, automatic categoriz...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	194,243
2406.14882	70B-parameter large language models in Japanese medical question-answering	Since the rise of large language models (LLMs), the domain adaptation has been one of the hot topics in various domains. Many medical LLMs trained with English medical dataset have made public recently. However, Japanese LLMs in medical domain still lack its research. Here we utilize multiple 70B-parameter LLMs for the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	466,516
2011.00704	Semi-supervised Autoencoding Projective Dependency Parsing	We describe two end-to-end autoencoding models for semi-supervised graph-based projective dependency parsing. The first model is a Locally Autoencoding Parser (LAP) encoding the input using continuous latent variables in a sequential manner; The second model is a Globally Autoencoding Parser (GAP) encoding the input in...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	204,335
2408.07650	Exact Trajectory Similarity Search With N-tree: An Efficient Metric Index for kNN and Range Queries	Similarity search is the problem of finding in a collection of objects those that are similar to a given query object. It is a fundamental problem in modern applications and the objects considered may be as diverse as locations in space, text documents, images, twitter messages, or trajectories of moving objects. In ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	true	480,675
2311.18774	An Aliasing-Free Hybrid Digital-Analog Polyphonic Synthesizer	Analog subtractive synthesizers are generally considered to provide superior sound quality compared to digital emulations. However, analog circuitry requires calibration and suffers from aging, temperature instability, and limited flexibility in generating a wide variety of waveforms. Digital synthesis can mitigate man...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	411,811
2305.06558	Segment and Track Anything	This report presents a framework called Segment And Track Anything (SAMTrack) that allows users to precisely and effectively segment and track any object in a video. Additionally, SAM-Track employs multimodal interaction methods that enable users to select multiple objects in videos for tracking, corresponding to their...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	363,581
2410.05552	Optimal Adaptive Experimental Design for Estimating Treatment Effect	Given n experiment subjects with potentially heterogeneous covariates and two possible treatments, namely active treatment and control, this paper addresses the fundamental question of determining the optimal accuracy in estimating the treatment effect. Furthermore, we propose an experimental design that approaches thi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	495,784
2407.12797	CEBench: A Benchmarking Toolkit for the Cost-Effectiveness of LLM Pipelines	Online Large Language Model (LLM) services such as ChatGPT and Claude 3 have transformed business operations and academic research by effortlessly enabling new opportunities. However, due to data-sharing restrictions, sectors such as healthcare and finance prefer to deploy local LLM applications using costly hardware r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	474,081
1905.03966	Memory-Attended Recurrent Network for Video Captioning	Typical techniques for video captioning follow the encoder-decoder framework, which can only focus on one source video being processed. A potential disadvantage of such design is that it cannot capture the multiple visual context information of a word appearing in more than one relevant videos in training data. To tack...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,334
2011.01434	"You eat with your eyes first": Optimizing Yelp Image Advertising	A business's online, photographic representation can play a crucial role in its success or failure. We use Yelp's image dataset and star-based review system as a measurement of an image's effectiveness in promoting a business. After preprocessing the Yelp dataset, we use transfer learning to train a classifier which ac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	204,580
0910.1511	Cooperation with an Untrusted Relay: A Secrecy Perspective	We consider the communication scenario where a source-destination pair wishes to keep the information secret from a relay node despite wanting to enlist its help. For this scenario, an interesting question is whether the relay node should be deployed at all. That is, whether cooperation with an untrusted relay node can...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,677
1711.05893	On Communication Complexity of Classification Problems	This work studies distributed learning in the spirit of Yao's model of communication complexity: consider a two-party setting, where each of the players gets a list of labelled examples and they communicate in order to jointly perform some learning task. To naturally fit into the framework of learning theory, the playe...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	84,667
2303.04231	A topological classifier to characterize brain states: When shape matters more than variance	Despite the remarkable accuracies attained by machine learning classifiers to separate complex datasets in a supervised fashion, most of their operation falls short to provide an informed intuition about the structure of data, and, what is more important, about the phenomena being characterized by the given datasets. B...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	350,002
2006.15553	DHARI Report to EPIC-Kitchens 2020 Object Detection Challenge	In this report, we describe the technical details of oursubmission to the EPIC-Kitchens Object Detection Challenge.Duck filling and mix-up techniques are firstly introduced to augment the data and significantly improve the robustness of the proposed method. Then we propose GRE-FPN and Hard IoU-imbalance Sampler methods...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	184,544
2411.00927	ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents	Large language model (LLM)-based agents have been increasingly used to interact with external environments (e.g., games, APIs, etc.) and solve tasks. However, current frameworks do not enable these agents to work with users and interact with them to align on the details of their tasks and reach user-defined goals; inst...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	504,840
2012.12186	Learning to Play Imperfect-Information Games by Imitating an Oracle Planner	We consider learning to play multiplayer imperfect-information games with simultaneous moves and large state-action spaces. Previous attempts to tackle such challenging games have largely focused on model-free learning methods, often requiring hundreds of years of experience to produce competitive agents. Our approach ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	212,854
2204.04541	KOBEST: Korean Balanced Evaluation of Significant Tasks	A well-formulated benchmark plays a critical role in spurring advancements in the natural language processing (NLP) field, as it allows objective and precise evaluation of diverse models. As modern language models (LMs) have become more elaborate and sophisticated, more difficult benchmarks that require linguistic know...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	290,693
2006.11488	Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning	Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveragin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,258
2412.11423	Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models	Recent advancements in diffusion models revolutionize image generation but pose risks of misuse, such as replicating artworks or generating deepfakes. Existing image protection methods, though effective, struggle to balance protection efficacy, invisibility, and latency, thus limiting practical use. We introduce pertur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	517,400
0908.3551	Level Crossing Rate and Average Fade Duration of EGC Systems with Cochannel Interference in Rayleigh Fading	Both the first-order signal statistics (e.g. the outage probability) and the second-order signal statistics (e.g. the average level crossing rate, LCR, and the average fade duration, AFD) are important design criteria and performance measures for the wireless communication systems, including the equal gain combining (E...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,331
1906.09769	Fault Matters: Sensor Data Fusion for Detection of Faults using Dempster-Shafer Theory of Evidence in IoT-Based Applications	Fault detection in sensor nodes is a pertinent issue that has been an important area of research for a very long time. But it is not explored much as yet in the context of Internet of Things. Internet of Things work with a massive amount of data so the responsibility for guaranteeing the accuracy of the data also lies ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	136,262
2406.12529	LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation	As the demand for more personalized recommendation grows and a dramatic boom in commercial scenarios arises, the study on multi-scenario recommendation (MSR) has attracted much attention, which uses the data from all scenarios to simultaneously improve their recommendation performance. However, existing methods tend to...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	465,448
1408.1147	Classification of the Z2Z4-linear Hadamard codes and their automorphism groups	A $Z_2Z_4$-linear Hadamard code of length $\alpha+2\beta=2^t$ is a binary Hadamard code which is the Gray map image of a $Z_2Z_4$-additive code with $\alpha$ binary coordinates and $\beta$ quaternary coordinates. It is known that there are exactly $[(t-1)/2]$ and $[t/2]$ nonequivalent $Z_2Z_4$-linear Hadamard codes of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,135
1707.02385	Evaluating Social Networks Using Task-Focused Network Inference	Networks are representations of complex underlying social processes. However, the same given network may be more suitable to model one behavior of individuals than another. In many cases, aggregate population models may be more effective than modeling on the network. We present a general framework for evaluating the su...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	76,693
2012.04713	Classical symmetries and the Quantum Approximate Optimization Algorithm	We study the relationship between the Quantum Approximate Optimization Algorithm (QAOA) and the underlying symmetries of the objective function to be optimized. Our approach formalizes the connection between quantum symmetry properties of the QAOA dynamics and the group of classical symmetries of the objective function...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	210,536
2211.16452	Interactive-Rate Supervisory Control for Arbitrarily-Routed Multi-Tendon Robots via Motion Planning	Tendon-driven robots, where one or more tendons under tension bend and manipulate a flexible backbone, can improve minimally invasive surgeries involving difficult-to-reach regions in the human body. Planning motions safely within constrained anatomical environments requires accuracy and efficiency in shape estimation ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	333,636
2002.10696	Human Perception-Optimized Planning for Comfortable VR-Based Telepresence	This paper introduces an emerging motion planning problem by considering a human that is immersed into the viewing perspective of a remote robot. The challenge is to make the experience both effective (such as delivering a sense of presence) and comfortable (such as avoiding adverse sickness symptoms, including nausea)...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	165,486
2402.12265	On the Byzantine-Resilience of Distillation-Based Federated Learning	Federated Learning (FL) algorithms using Knowledge Distillation (KD) have received increasing attention due to their favorable properties with respect to privacy, non-i.i.d. data and communication cost. These methods depart from transmitting model parameters and instead communicate information about a learning task by ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	430,779
2101.05456	Self-Supervised Learning for Segmentation	Self-supervised learning is emerging as an effective substitute for transfer learning from large datasets. In this work, we use kidney segmentation to explore this idea. The anatomical asymmetry of kidneys is leveraged to define an effective proxy task for kidney segmentation via self-supervised learning. A siamese con...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,432
2204.11786	Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications	Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI), including computer vision, natural language processing and speech recognition. However, their superior performance comes at the considerable cost of computational complexity, which greatly hinders their applica...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	293,257
2109.01876	Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting	Neural networks inspired by differential equations have proliferated for the past several years. Neural ordinary differential equations (NODEs) and neural controlled differential equations (NCDEs) are two representative examples of them. In theory, NCDEs provide better representation learning capability for time-series...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	253,564
1901.00361	Optical Fringe Patterns Filtering Based on Multi-Stage Convolution Neural Network	Optical fringe patterns are often contaminated by speckle noise, making it difficult to accurately and robustly extract their phase fields. To deal with this problem, we propose a filtering method based on deep learning, called optical fringe patterns denoising convolutional neural network (FPD-CNN), for directly remov...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	117,752
cs/0108013	Convergent Approximate Solving of First-Order Constraints by Approximate Quantifiers	Exactly solving first-order constraints (i.e., first-order formulas over a certain predefined structure) can be a very hard, or even undecidable problem. In continuous structures like the real numbers it is promising to compute approximate solutions instead of exact ones. However, the quantifiers of the first-order pre...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	537,406
2309.09826	Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding	Auto-completing code enables developers to speed up coding significantly. Recent advances in transformer-based large language model (LLM) technologies have been applied to code synthesis. However, studies show that many of such synthesized codes contain vulnerabilities. We propose a novel vulnerability-constrained deco...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	392,761
1806.10474	The challenge of realistic music generation: modelling raw audio at scale	Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musical...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	101,551
1606.03978	Optimisation Of Pressure Sewer Operation	The paper deals with the new control method developed for the pressure sewer systems. This method eliminates the disadvantages of currently common used on-off regulation. The major disadvantage is a transition of inconstancies of the effluent production into the sewage system. The propose method is primarily based on t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	57,175
cs/0609054	High Data-Rate Single-Symbol ML Decodable Distributed STBCs for Cooperative Networks	High data-rate Distributed Orthogonal Space-Time Block Codes (DOSTBCs) which achieve the single-symbol decodability and full diversity order are proposed in this paper. An upper bound of the data-rate of the DOSTBC is derived and it is approximately twice larger than that of the conventional repetition-based cooperativ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,686
2405.06653	A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules	The immune checkpoint inhibitors have demonstrated promising clinical efficacy across various tumor types, yet the percentage of patients who benefit from them remains low. The bindings between tumor antigens and HLA-I/TCR molecules determine the antigen presentation and T-cell activation, thereby playing an important ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	453,372
2109.01733	F3S: Free Flow Fever Screening	Identification of people with elevated body temperature can reduce or dramatically slow down the spread of infectious diseases like COVID-19. We present a novel fever-screening system, F3S, that uses edge machine learning techniques to accurately measure core body temperatures of multiple individuals in a free-flow set...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	253,516
2311.17351	Exploring Large Language Models for Human Mobility Prediction under Public Events	Public events, such as concerts and sports games, can be major attractors for large crowds, leading to irregular surges in travel demand. Accurate human mobility prediction for public events is thus crucial for event planning as well as traffic or crowd management. While rich textual descriptions about public events ar...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	411,267
2409.14248	Higher-order-ReLU-KANs (HRKANs) for solving physics-informed neural networks (PINNs) more accurately, robustly and faster	Finding solutions to partial differential equations (PDEs) is an important and essential component in many scientific and engineering discoveries. One of the common approaches empowered by deep learning is Physics-informed Neural Networks (PINNs). Recently, a new type of fundamental neural network model, Kolmogorov-Arn...	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	490,391
2405.05937	Dynamics of a Towed Cable with Sensor-Array for Underwater Target Motion Analysis	During a war situation, many times an underwater target motion analysis (TMA) is performed using bearing-only measurements, obtained from a sensor array, which is towed by an own-ship with the help of a connected cable. It is well known that the own-ship is required to perform a manoeuvre in order to make the system ob...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	453,112
1812.03699	Taxi Demand-Supply Forecasting: Impact of Spatial Partitioning on the Performance of Neural Networks	In this paper, we investigate the significance of choosing an appropriate tessellation strategy for a spatio-temporal taxi demand-supply modeling framework. Our study compares (i) the variable-sized polygon based Voronoi tessellation, and (ii) the fixed-sized grid based Geohash tessellation, using taxi demand-supply GP...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,074
2401.07174	On the (In)Compatibility between Group Fairness and Individual Fairness	We study the compatibility between the optimal statistical parity solutions and individual fairness. While individual fairness seeks to treat similar individuals similarly, optimal statistical parity aims to provide similar treatment to individuals who share relative similarity within their respective sensitive groups....	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	421,441
2411.05042	Improving Radiology Report Conciseness and Structure via Local Large Language Models	In this study, we aim to enhance radiology reporting by improving both the conciseness and structured organization of findings (also referred to as templating), specifically by organizing information according to anatomical regions. This structured approach allows physicians to locate relevant information quickly, incr...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	506,528
2502.08009	The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models	Decoder-only language models have the ability to dynamically switch between various computational tasks based on input prompts. Despite many successful applications of prompting, there is very limited understanding of the internal mechanism behind such flexibility. In this work, we investigate how different prompting m...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	532,860
2403.03281	Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits	We consider the problem of late multi-modal fusion for discriminative learning. Motivated by noisy, multi-source domains that require understanding the reliability of each data source, we explore the notion of credibility in the context of multi-modal fusion. We propose a combination function that uses probabilistic ci...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	435,132
2103.11920	Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval	Current state-of-the-art approaches to cross-modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. While offering unmatched retrieval performance, such models: 1) are typically pretrained from...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	226,009
2411.19292	UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation	Photorealistic 3D vehicle models with high controllability are essential for autonomous driving simulation and data augmentation. While handcrafted CAD models provide flexible controllability, free CAD libraries often lack the high-quality materials necessary for photorealistic rendering. Conversely, reconstructed 3D m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	512,191
2410.16533	Large Body Language Models	As virtual agents become increasingly prevalent in human-computer interaction, generating realistic and contextually appropriate gestures in real-time remains a significant challenge. While neural rendering techniques have made substantial progress with static scripts, their applicability to human-computer interactions...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	501,072
0902.3294	Progress in Computer-Assisted Inductive Theorem Proving by Human-Orientedness and Descente Infinie?	In this short position paper we briefly review the development history of automated inductive theorem proving and computer-assisted mathematical induction. We think that the current low expectations on progress in this field result from a faulty narrow-scope historical projection. Our main motivation is to explain--on ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	3,196
2306.16407	A proof of the Etzion-Silberstein conjecture for monotone and MDS-constructible Ferrers diagrams	Ferrers diagram rank-metric codes were introduced by Etzion and Silberstein in 2009. In their work, they proposed a conjecture on the largest dimension of a space of matrices over a finite field whose nonzero elements are supported on a given Ferrers diagram and all have rank lower bounded by a fixed positive integer $...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	376,356
2501.08180	D$^2$-DPM: Dual Denoising for Quantized Diffusion Probabilistic Models	Diffusion models have achieved cutting-edge performance in image generation. However, their lengthy denoising process and computationally intensive score estimation network impede their scalability in low-latency and resource-constrained scenarios. Post-training quantization (PTQ) compresses and accelerates diffusion m...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	524,658
2102.01033	Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors	Recent inroads in Computer Vision (CV) and Machine Learning (ML) have motivated a new approach to the analysis of particle imaging detector data. Unlike previous efforts which tackled isolated CV tasks, this paper introduces an end-to-end, ML-based data reconstruction chain for Liquid Argon Time Projection Chambers (LA...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	217,984
2312.00621	Weighted Riesz Particles	Markov chain Monte Carlo (MCMC) methods are simulated by local exploration of complex statistical distributions, and while bypassing the cumbersome requirement of a specific analytical expression for the target, this stochastic exploration of an uncertain parameter space comes at the expense of a large number of sample...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	412,111
1803.08085	Probabilistic Video Generation using Holistic Attribute Control	Videos express highly structured spatio-temporal patterns of visual data. A video can be thought of as being governed by two factors: (i) temporally invariant (e.g., person identity), or slowly varying (e.g., activity), attribute-induced appearance, encoding the persistent content of each frame, and (ii) an inter-frame...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,190
cs/0508103	Corpus-based Learning of Analogies and Semantic Relations	We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the SAT college entrance exam. A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D"; for example, mason:stone::carpen...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	538,912
1909.10206	Cross Z-Complementary Pairs for Optimal Training in Spatial Modulation over Frequency Selective Channels	The contributions of this paper are twofold: Firstly, we introduce a novel class of sequence pairs, called "cross Z-complementary pairs (CZCPs)", each displaying zero-correlation zone (ZCZ) properties for both their aperiodic autocorrelation sums and crosscorrelation sums. Systematic constructions of perfect CZCPs base...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	146,479
2204.09777	Multi-Focus Image Fusion based on Gradient Transform	Multi-focus image fusion is a challenging field of study that aims to provide a completely focused image by integrating focused and un-focused pixels. Most existing methods suffer from shift variance, misregistered images, and data-dependent. In this study, we introduce a novel gradient information-based multi-focus im...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,552
2311.13852	A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression	The lack of explainability using relevant clinical knowledge hinders the adoption of Artificial Intelligence-powered analysis of unstructured clinical dialogue. A wealth of relevant, untapped Mental Health (MH) data is available in online communities, providing the opportunity to address the explainability problem with...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	409,898
2302.05109	Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection	Deep learning-based change detection (CD) using remote sensing images has received increasing attention in recent years. However, how to effectively extract and fuse the deep features of bi-temporal images for improving the accuracy of CD is still a challenge. To address that, a novel adjacent-level feature fusion netw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	344,937
1608.06386	Which techniques does your application use?: An information extraction framework for scientific articles	Every field of research consists of multiple application areas with various techniques routinely used to solve problems in these wide range of application areas. With the exponential growth in research volumes, it has become difficult to keep track of the ever-growing number of application areas as well as the correspo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	60,107
1511.06653	Recurrent Semi-supervised Classification and Constrained Adversarial Generation with Motion Capture Data	We explore recurrent encoder multi-decoder neural network architectures for semi-supervised sequence classification and reconstruction. We find that the use of multiple reconstruction modules helps models generalize in a classification task when only a small amount of labeled data is available, which is often the case ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	49,297
1812.06387	Pre-Trained Convolutional Neural Network Features for Facial Expression Recognition	Facial expression recognition has been an active area in computer vision with application areas including animation, social robots, personalized banking, etc. In this study, we explore the problem of image classification for detecting facial expressions based on features extracted from pre-trained convolutional neural ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	116,603
2308.01682	Evaluating Link Prediction Explanations for Graph Neural Networks	Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains. Providing human-understandable explanations for GML models is a challenging yet fundamental task to foster their adoption, but validating explanations for link prediction models has rece...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	383,331
2210.07932	Neural Routing in Meta Learning	Meta-learning often referred to as learning-to-learn is a promising notion raised to mimic human learning by exploiting the knowledge of prior tasks but being able to adapt quickly to novel tasks. A plethora of models has emerged in this context and improved the learning efficiency, robustness, etc. The question that a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	323,923
2310.02439	Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions	We propose novel evaluations for mathematical reasoning capabilities of Large Language Models (LLMs) based on mathematical misconceptions. Our primary approach is to simulate LLMs as a novice learner and an expert tutor, aiming to identify the incorrect answer to math question resulted from a specific misconception and...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	396,848
2411.00463	The learned range test method for the inverse inclusion problem	We consider the inverse problem consisting of the reconstruction of an inclusion $B$ contained in a bounded domain $\Omega\subset\mathbb{R}^d$ from a single pair of Cauchy data $(u\|_{\partial\Omega},\partial_\nu u\|_{\partial\Omega})$, where $\Delta u=0$ in $\Omega\setminus\overline B$ and $u=0$ on $\partial B$. We show...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	504,609
1908.09021	Geometrical Regret Matching	We argue that the existing regret matchings for Nash equilibrium approximation conduct "jumpy" strategy updating when the probabilities of future plays are set to be proportional to positive regret measures. We propose a geometrical regret matching which features "smooth" strategy updating. Our approach is simple, intu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	142,728
2012.13685	Discovering Closed and Maximal Embedded Patterns from Large Tree Data	We address the problem of summarizing embedded tree patterns extracted from large data trees. We do so by defining and mining closed and maximal embedded unordered tree patterns from a single large data tree. We design an embedded frequent pattern mining algorithm extended with a local closedness checking technique. Th...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	213,299

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