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
2204.13154	Attention Mechanism in Neural Networks: Where it Comes and Where it Goes	A long time ago in the machine learning literature, the idea of incorporating a mechanism inspired by the human visual system into neural networks was introduced. This idea is named the attention mechanism, and it has gone through a long development period. Today, many works have been devoted to this idea in a variety ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	293,713
1408.4102	Estimation of Monotone Treatment Effects in Network Experiments	Randomized experiments on social networks pose statistical challenges, due to the possibility of interference between units. We propose new methods for estimating attributable treatment effects in such settings. The methods do not require partial interference, but instead require an identifying assumption that is simil...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	35,440
2202.01627	Non-Vacuous Generalisation Bounds for Shallow Neural Networks	We focus on a specific class of shallow neural networks with a single hidden layer, namely those with $L_2$-normalised data and either a sigmoid-shaped Gaussian error function ("erf") activation or a Gaussian Error Linear Unit (GELU) activation. For these networks, we derive new generalisation bounds through the PAC-Ba...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,533
1110.4414	(1+eps)-approximate Sparse Recovery	The problem central to sparse recovery and compressive sensing is that of stable sparse recovery: we want a distribution of matrices A in R^{m\times n} such that, for any x \in R^n and with probability at least 2/3 over A, there is an algorithm to recover x* from Ax with \|\|x* - x\|\|_p <= C min_{k-sparse x'} \|\|x - x'\|\|...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	12,720
2107.10599	Towards Explaining Adversarial Examples Phenomenon in Artificial Neural Networks	In this paper, we study the adversarial examples existence and adversarial training from the standpoint of convergence and provide evidence that pointwise convergence in ANNs can explain these observations. The main contribution of our proposal is that it relates the objective of the evasion attacks and adversarial tra...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	247,341
2208.14706	Transfering Low-Frequency Features for Domain Adaptation	Previous unsupervised domain adaptation methods did not handle the cross-domain problem from the perspective of frequency for computer vision. The images or feature maps of different domains can be decomposed into the low-frequency component and high-frequency component. This paper proposes the assumption that low-freq...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,402
2208.05040	Economics of Semantic Communication System: An Auction Approach	Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meaning of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sens...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	312,296
1703.04727	Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction	The visual focus of attention (VFOA) has been recognized as a prominent conversational cue. We are interested in estimating and tracking the VFOAs associated with multi-party social interactions. We note that in this type of situations the participants either look at each other or at an object of interest; therefore th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,940
2501.00199	GPT-4 on Clinic Depression Assessment: An LLM-Based Pilot Study	Depression has impacted millions of people worldwide and has become one of the most prevalent mental disorders. Early mental disorder detection can lead to cost savings for public health agencies and avoid the onset of other major comorbidities. Additionally, the shortage of specialized personnel is a critical issue be...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	521,570
2407.20139	Emission Reduction in Urban Environments by Replacing Conventional City Buses with Electric Bus Technology: A Case Study of Pakistan	The global transportation industry has become one of the main contributors to air pollution. Consequently, electric buses and green transportation are gaining popularity as crucial steps to reduce emission concerns. Many developed countries have already adopted the concept of Battery Electric Buses (BEBs), while the de...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	477,057
2409.18408	Query matching for spatio-temporal action detection with query-based object detector	In this paper, we propose a method that extends the query-based object detection model, DETR, to spatio-temporal action detection, which requires maintaining temporal consistency in videos. Our proposed method applies DETR to each frame and uses feature shift to incorporate temporal information. However, DETR's object ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	492,240
2107.07397	Level generation and style enhancement -- deep learning for game development overview	We present practical approaches of using deep learning to create and enhance level maps and textures for video games -- desktop, mobile, and web. We aim to present new possibilities for game developers and level artists. The task of designing levels and filling them with details is challenging. It is both time-consumin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	246,405
2202.05843	Fast Model-based Policy Search for Universal Policy Networks	Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning. Although recent approaches such as universal policy networks partially address this issue by enabling the storage of multiple policies trained in simulation on a wide range of dynamic/laten...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	280,011
2105.01238	Supervised multi-specialist topic model with applications on large-scale electronic health record data	Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled. We present MixEHR-S to jointly infer specialist-disease topics f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	233,465
2101.09986	Multi-view Integration Learning for Irregularly-sampled Clinical Time Series	Electronic health record (EHR) data is sparse and irregular as it is recorded at irregular time intervals, and different clinical variables are measured at each observation point. In this work, we propose a multi-view features integration learning from irregular multivariate time series data by self-attention mechanism...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	216,781
2403.16170	Voltage Regulation in Polymer Electrolyte Fuel Cell Systems Using Gaussian Process Model Predictive Control	This study introduces a novel approach utilizing Gaussian process model predictive control (MPC) to stabilize the output voltage of a polymer electrolyte fuel cell (PEFC) system by simultaneously regulating hydrogen and airflow rates. Two Gaussian process models are developed to capture PEFC dynamics, taking into accou...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	440,908
1512.07980	Diversity Enhancement for Micro-Differential Evolution	The differential evolution (DE) algorithm suffers from high computational time due to slow nature of evaluation. In contrast, micro-DE (MDE) algorithms employ a very small population size, which can converge faster to a reasonable solution. However, these algorithms are vulnerable to a premature convergence as well as ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	50,473
2402.04812	Aspect-Based Sentiment Analysis for Open-Ended HR Survey Responses	Understanding preferences, opinions, and sentiment of the workforce is paramount for effective employee lifecycle management. Open-ended survey responses serve as a valuable source of information. This paper proposes a machine learning approach for aspect-based sentiment analysis (ABSA) of Dutch open-ended responses in...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	427,595
2401.13796	Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning	Machine Learning (ML) has revolutionized various domains, offering predictive capabilities in several areas. However, with the increasing accessibility of ML tools, many practitioners, lacking deep ML expertise, adopt a "push the button" approach, utilizing user-friendly interfaces without a thorough understanding of u...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	423,854
2302.14406	Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Dataset	In visual instruction-following dialogue games, players can engage in repair mechanisms in face of an ambiguous or underspecified instruction that cannot be fully mapped to actions in the world. In this work, we annotate Instruction Clarification Requests (iCRs) in CoDraw, an existing dataset of interactions in a multi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	348,282
1810.02866	Artificial Intelligence Assisted Power Grid Hardening in Response to Extreme Weather Events	In this paper, an artificial intelligence based grid hardening model is proposed with the objective of improving power grid resilience in response to extreme weather events. At first, a machine learning model is proposed to predict the component states (either operational or outage) in response to the extreme event. Th...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	109,676
2205.13163	Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs	This work discusses tensor network embeddings, which are random matrices ($S$) with tensor network structure. These embeddings have been used to perform dimensionality reduction of tensor network structured inputs $x$ and accelerate applications such as tensor decomposition and kernel regression. Existing works have de...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	298,833
1601.05353	On the complexity of bounded time and precision reachability for piecewise affine systems	Reachability for piecewise affine systems is known to be undecidable, starting from dimension $2$. In this paper we investigate the exact complexity of several decidable variants of reachability and control questions for piecewise affine systems. We show in particular that the region to region bounded time versions lea...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	51,116
2006.02708	Auto-Rectify Network for Unsupervised Indoor Depth Estimation	Single-View depth estimation using the CNNs trained from unlabelled videos has shown significant promise. However, excellent results have mostly been obtained in street-scene driving scenarios, and such methods often fail in other settings, particularly indoor videos taken by handheld devices. In this work, we establis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	180,121
1403.7948	Structure of conflict graphs in constraint alignment problems and algorithms	We consider the constrained graph alignment problem which has applications in biological network analysis. Given two input graphs $G_1=(V_1,E_1), G_2=(V_2,E_2)$, a pair of vertex mappings induces an {\it edge conservation} if the vertex pairs are adjacent in their respective graphs. %In general terms The goal is to pro...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	31,954
1907.06817	Energy-efficient Alternating Iterative Secure Structure of Maximizing Secrecy Rate for Directional Modulation Networks	In a directional modulation (DM) network, the issues of security and privacy have taken on an increasingly important role. Since the power allocation of confidential message and artificial noise will make a constructive effect on the system performance, it is important to jointly consider the relationship between the b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	138,710
2408.01928	A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce	Query intent classification is an essential module for customers to find desired products on the e-commerce application quickly. Most existing query intent classification methods rely on the users' click behavior as a supervised signal to construct training samples. However, these methods based entirely on posterior la...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	478,422
1711.01345	Computationally efficient cardiac views projection using 3D Convolutional Neural Networks	4D Flow is an MRI sequence which allows acquisition of 3D images of the heart. The data is typically acquired volumetrically, so it must be reformatted to generate cardiac long axis and short axis views for diagnostic interpretation. These views may be generated by placing 6 landmarks: the left and right ventricle apex...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	83,864
2206.09032	Conjunctive Queries with Free Access Patterns under Updates	We study the problem of answering conjunctive queries with free access patterns (CQAPs) under updates. A free access pattern is a partition of the free variables of the query into input and output. The query returns tuples over the output variables given a tuple of values over the input variables. We introduce a full...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	303,402
2412.06777	Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving	Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception. Most existing methods rely on depth estimation through self-supervision or multi-modality sensor fusion. In this paper, we propose Driv3R, a DUSt3R-based framework that directly regresses per-frame point maps fr...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	515,370
1908.03142	The Hitchhiker's Guide to LDA	Latent Dirichlet Allocation (LDA) model is a famous model in the topic model field, it has been studied for years due to its extensive application value in industry and academia. However, the mathematical derivation of LDA model is challenging and difficult, which makes it difficult for the beginners to learn. To help ...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	141,169
2310.17168	Learning an Inventory Control Policy with General Inventory Arrival Dynamics	In this paper we address the problem of learning and backtesting inventory control policies in the presence of general arrival dynamics -- which we term as a quantity-over-time arrivals model (QOT). We also allow for order quantities to be modified as a post-processing step to meet vendor constraints such as order mini...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,026
2410.04690	SegINR: Segment-wise Implicit Neural Representation for Sequence Alignment in Neural Text-to-Speech	We present SegINR, a novel approach to neural Text-to-Speech (TTS) that addresses sequence alignment without relying on an auxiliary duration predictor and complex autoregressive (AR) or non-autoregressive (NAR) frame-level sequence modeling. SegINR simplifies the process by converting text sequences directly into fram...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	495,398
2103.14215	The Complete Affine Automorphism Group of Polar Codes	Recently, a permutation-based successive cancellation (PSC) decoding framework for polar codes attaches much attention. It decodes several permuted codewords with independent successive cancellation (SC) decoders. Its latency thus can be reduced to that of SC decoding. However, the PSC framework is ineffective for perm...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	226,770
2104.10116	Detection of Audio-Video Synchronization Errors Via Event Detection	We present a new method and a large-scale database to detect audio-video synchronization(A/V sync) errors in tennis videos. A deep network is trained to detect the visual signature of the tennis ball being hit by the racquet in the video stream. Another deep network is trained to detect the auditory signature of the sa...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	231,469
1106.4064	Algorithmic Programming Language Identification	Motivated by the amount of code that goes unidentified on the web, we introduce a practical method for algorithmically identifying the programming language of source code. Our work is based on supervised learning and intelligent statistical features. We also explored, but abandoned, a grammatical approach. In testing, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	10,925
1909.04802	Variable Rate Deep Image Compression With a Conditional Autoencoder	In this paper, we propose a novel variable-rate learned image compression framework with a conditional autoencoder. Previous learning-based image compression methods mostly require training separate networks for different compression rates so they can yield compressed images of varying quality. In contrast, we train an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	144,901
2408.17344	rerankers: A Lightweight Python Library to Unify Ranking Methods	This paper presents rerankers, a Python library which provides an easy-to-use interface to the most commonly used re-ranking approaches. Re-ranking is an integral component of many retrieval pipelines; however, there exist numerous approaches to it, relying on different implementation methods. rerankers unifies these m...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	484,669
2203.11075	Dense Siamese Network for Dense Unsupervised Learning	This paper presents Dense Siamese Network (DenseSiam), a simple unsupervised learning framework for dense prediction tasks. It learns visual representations by maximizing the similarity between two views of one image with two types of consistency, i.e., pixel consistency and region consistency. Concretely, DenseSiam fi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	286,787
2106.12894	InFlow: Robust outlier detection utilizing Normalizing Flows	Normalizing flows are prominent deep generative models that provide tractable probability distributions and efficient density estimation. However, they are well known to fail while detecting Out-of-Distribution (OOD) inputs as they directly encode the local features of the input representations in their latent space. I...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	242,876
2402.01093	Need a Small Specialized Language Model? Plan Early!	Large language models are versatile tools but are not suitable for small inference budgets. Small models have more efficient inference, but their lower capacity means that their performance can be good only if one limits their scope to a specialized domain. This paper explores how to get good specialized small language...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	425,853
2108.00373	SPEAR : Semi-supervised Data Programming in Python	We present SPEAR, an open-source python library for data programming with semi supervision. The package implements several recent data programming approaches including facility to programmatically label and build training data. SPEAR facilitates weak supervision in the form of heuristics (or rules) and association of n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	248,693
2302.02338	Electromechanical phase-field fracture modelling of piezoresistive CNT-based composites	We present a novel computational framework to simulate the electromechanical response of self-sensing carbon nanotube (CNT)-based composites experiencing fracture. The computational framework combines electrical-deformation-fracture finite element modelling with a mixed micromechanics formulation. The latter is used to...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	343,966
2308.00263	Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation	Asynchronous Federated Learning with Buffered Aggregation (FedBuff) is a state-of-the-art algorithm known for its efficiency and high scalability. However, it has a high communication cost, which has not been examined with quantized communications. To tackle this problem, we present a new algorithm (QAFeL), with a quan...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	382,878
2406.06592	Improve Mathematical Reasoning in Language Models by Automated Process Supervision	Complex multi-step reasoning tasks, such as solving mathematical problems or generating code, remain a significant hurdle for even the most advanced large language models (LLMs). Verifying LLM outputs with an Outcome Reward Model (ORM) is a standard inference-time technique aimed at enhancing the reasoning performance ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	462,675
2303.09941	Leaping Into Memories: Space-Time Deep Feature Synthesis	The success of deep learning models has led to their adaptation and adoption by prominent video understanding methods. The majority of these approaches encode features in a joint space-time modality for which the inner workings and learned representations are difficult to visually interpret. We propose LEArned Preconsc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,248
2012.13604	DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approach	Domain Name System (DNS) is a crucial component of current IP-based networks as it is the standard mechanism for name to IP resolution. However, due to its lack of data integrity and origin authentication processes, it is vulnerable to a variety of attacks. One such attack is Typosquatting. Detecting this attack is par...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	213,267
2007.02684	On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection	Face morphing attacks have raised critical concerns as they demonstrate a new vulnerability of Face Recognition Systems (FRS), which are widely deployed in border control applications. The face morphing process uses the images from multiple data subjects and performs an image blending operation to generate a morphed im...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	185,820
1709.07598	Demography-based Facial Retouching Detection using Subclass Supervised Sparse Autoencoder	Digital retouching of face images is becoming more widespread due to the introduction of software packages that automate the task. Several researchers have introduced algorithms to detect whether a face image is original or retouched. However, previous work on this topic has not considered whether or how accuracy of re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,310
1807.11878	FADE: Fast and Asymptotically efficient Distributed Estimator for dynamic networks	Consider a set of agents that wish to estimate a vector of parameters of their mutual interest. For this estimation goal, agents can sense and communicate. When sensing, an agent measures (in additive gaussian noise) linear combinations of the unknown vector of parameters. When communicating, an agent can broadcast inf...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	104,266
2109.08290	Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming	We propose a method for generating explainable rule sets from tree-ensemble learners using Answer Set Programming (ASP). To this end, we adopt a decompositional approach where the split structures of the base decision trees are exploited in the construction of rules, which in turn are assessed using pattern mining meth...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	255,845
2407.18060	Cross-Vendor Reproducibility of Radiomics-based Machine Learning Models for Computer-aided Diagnosis	Background: The reproducibility of machine-learning models in prostate cancer detection across different MRI vendors remains a significant challenge. Methods: This study investigates Support Vector Machines (SVM) and Random Forest (RF) models trained on radiomic features extracted from T2-weighted MRI images using Pyra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	476,231
2408.14348	Deep learning-based ecological analysis of camera trap images is impacted by training data quality and size	Large wildlife image collections from camera traps are crucial for biodiversity monitoring, offering insights into species richness, occupancy, and activity patterns. However, manual processing of these data is time-consuming, hindering analytical processes. To address this, deep neural networks have been widely adopte...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	483,502
2211.13529	3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection	Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection. One key challenge in camera-LiDAR fusion involves mitigating the large domain gap between the two sensors in terms of coordinates and data distribution when fusing their features. In this paper, we propose a nove...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	332,509
2308.03239	Asynchronous Decentralized Q-Learning: Two Timescale Analysis By Persistence	Non-stationarity is a fundamental challenge in multi-agent reinforcement learning (MARL), where agents update their behaviour as they learn. Many theoretical advances in MARL avoid the challenge of non-stationarity by coordinating the policy updates of agents in various ways, including synchronizing times at which agen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	383,950
2006.00025	Environmental regulation using Plasticoding for the evolution of robots	Evolutionary robot systems are usually affected by the properties of the environment indirectly through selection. In this paper, we present and investigate a system where the environment also has a direct effect: through regulation. We propose a novel robot encoding method where a genotype encodes multiple possible ph...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	179,342
1708.00549	Improved Representation Learning for Predicting Commonsense Ontologies	Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints. We explore two extensions of one such model, the order-embedding model for hierarchical...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	78,224
1710.10400	Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition	Text in curve orientation, despite being one of the common text orientations in real world environment, has close to zero existence in well received scene text datasets such as ICDAR2013 and MSRA-TD500. The main motivation of Total-Text is to fill this gap and facilitate a new research direction for the scene text comm...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	83,376
2408.10819	GS-KGC: A Generative Subgraph-based Framework for Knowledge Graph Completion with Large Language Models	Knowledge graph completion (KGC) focuses on identifying missing triples in a knowledge graph (KG) , which is crucial for many downstream applications. Given the rapid development of large language models (LLMs), some LLM-based methods are proposed for KGC task. However, most of them focus on prompt engineering while ov...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	482,036
2009.10692	TSV Extrusion Morphology Classification Using Deep Convolutional Neural Networks	In this paper, we utilize deep convolutional neural networks (CNNs) to classify the morphology of through-silicon via (TSV) extrusion in three dimensional (3D) integrated circuits (ICs). TSV extrusion is a crucial reliability concern which can deform and crack interconnect layers in 3D ICs and cause device failures. He...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	196,964
2407.06817	AstroSpy: On detecting Fake Images in Astronomy via Joint Image-Spectral Representations	The prevalence of AI-generated imagery has raised concerns about the authenticity of astronomical images, especially with advanced text-to-image models like Stable Diffusion producing highly realistic synthetic samples. Existing detection methods, primarily based on convolutional neural networks (CNNs) or spectral anal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	471,546
2310.02877	Stationarity without mean reversion in improper Gaussian processes	The behavior of a GP regression depends on the choice of covariance function. Stationary covariance functions are preferred in machine learning applications. However, (non-periodic) stationary covariance functions are always mean reverting and can therefore exhibit pathological behavior when applied to data that does n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	397,026
1807.08476	Human peripheral blur is optimal for object recognition	Our vision is sharpest at the center of our gaze and becomes progressively blurry into the periphery. It is widely believed that this high foveal resolution evolved at the expense of peripheral acuity. But what if this sampling scheme is actually optimal for object recognition? To test this hypothesis, we trained deep ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,550
2409.12973	The Era of Foundation Models in Medical Imaging is Approaching : A Scoping Review of the Clinical Value of Large-Scale Generative AI Applications in Radiology	Social problems stemming from the shortage of radiologists are intensifying, and artificial intelligence is being highlighted as a potential solution. Recently emerging large-scale generative AI has expanded from large language models (LLMs) to multi-modal models, showing potential to revolutionize the entire process o...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	489,799
2404.04904	Cross-Domain Audio Deepfake Detection: Dataset and Analysis	Audio deepfake detection (ADD) is essential for preventing the misuse of synthetic voices that may infringe on personal rights and privacy. Recent zero-shot text-to-speech (TTS) models pose higher risks as they can clone voices with a single utterance. However, the existing ADD datasets are outdated, leading to subopti...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	444,853
2009.03091	Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method	We present a novel method for inferring ground-truth signal from multiple degraded signals, affected by different amounts of sensor exposure. The algorithm learns a multiplicative degradation effect by performing iterative corrections of two signals solely from the ratio between them. The degradation function d should ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	194,741
2004.01302	Distributed Inference with Sparse and Quantized Communication	We consider the problem of distributed inference where agents in a network observe a stream of private signals generated by an unknown state, and aim to uniquely identify this state from a finite set of hypotheses. We focus on scenarios where communication between agents is costly, and takes place over channels with fi...	false	false	false	false	false	false	true	false	false	true	true	false	false	false	false	false	false	true	170,875
2410.08551	Context-Aware Full Body Anonymization using Text-to-Image Diffusion Models	Anonymization plays a key role in protecting sensible information of individuals in real world datasets. Self-driving cars for example need high resolution facial features to track people and their viewing direction to predict future behaviour and react accordingly. In order to protect people's privacy whilst keeping i...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	497,164
2108.11859	Binary sequences with length n and nonlinear complexity not less than n/2	In this paper, the construction of finite-length binary sequences whose nonlinear complexity is not less than half of the length is investigated. By characterizing the structure of the sequences, an algorithm is proposed to generate all binary sequences with length $n$ and nonlinear complexity $c_{n}\geq n/2$, where $n...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	252,316
2112.13626	Generation of Synthetic Rat Brain MRI scans with a 3D Enhanced Alpha-GAN	Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are the availability of MRI scanners and the time required for a full sca...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	273,310
1710.10329	Lower Bounds for Higher-Order Convex Optimization	State-of-the-art methods in convex and non-convex optimization employ higher-order derivative information, either implicitly or explicitly. We explore the limitations of higher-order optimization and prove that even for convex optimization, a polynomial dependence on the approximation guarantee and higher-order smoothn...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,350
1505.05232	Multi-scale recognition with DAG-CNNs	We explore multi-scale convolutional neural nets (CNNs) for image classification. Contemporary approaches extract features from a single output layer. By extracting features from multiple layers, one can simultaneously reason about high, mid, and low-level features during classification. The resulting multi-scale archi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	43,279
1603.06541	A Comparison Study of Nonlinear Kernels	In this paper, we compare 5 different nonlinear kernels: min-max, RBF, fRBF (folded RBF), acos, and acos-$\chi^2$, on a wide range of publicly available datasets. The proposed fRBF kernel performs very similarly to the RBF kernel. Both RBF and fRBF kernels require an important tuning parameter ($\gamma$). Interestingly...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	53,510
2404.04517	Latent-based Diffusion Model for Long-tailed Recognition	Long-tailed imbalance distribution is a common issue in practical computer vision applications. Previous works proposed methods to address this problem, which can be categorized into several classes: re-sampling, re-weighting, transfer learning, and feature augmentation. In recent years, diffusion models have shown an ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	444,678
2207.05705	Conservative SPDEs as fluctuating mean field limits of stochastic gradient descent	The convergence of stochastic interacting particle systems in the mean-field limit to solutions of conservative stochastic partial differential equations is established, with optimal rate of convergence. As a second main result, a quantitative central limit theorem for such SPDEs is derived, again, with optimal rate of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	307,635
1906.01102	Do place cells dream of conditional probabilities? Learning Neural Nystr\"om representations	We posit that hippocampal place cells encode information about future locations under a transition distribution observed as an agent explores a given (physical or conceptual) space. The encoding of information about the current location, usually associated with place cells, then emerges as a necessary step to achieve t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	133,590
1608.03008	Network Topology Inference from Spectral Templates	We address the problem of identifying a graph structure from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. The fresh loo...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	59,624
2407.00886	Efficient Automated Circuit Discovery in Transformers using Contextual Decomposition	Automated mechanistic interpretation research has attracted great interest due to its potential to scale explanations of neural network internals to large models. Existing automated circuit discovery work relies on activation patching or its approximations to identify subgraphs in models for specific tasks (circuits). ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	469,039
2011.06259	Learning to Segment Dynamic Objects using SLAM Outliers	We present a method to automatically learn to segment dynamic objects using SLAM outliers. It requires only one monocular sequence per dynamic object for training and consists in localizing dynamic objects using SLAM outliers, creating their masks, and using these masks to train a semantic segmentation network. We inte...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	206,195
2412.10690	Affiliation-based Local Community Detection across Multiple Networks	Real-world networks are often constructed from different sources or domains, including various types of entities and diverse relationships between networks, thus forming multi-domain networks. A single network typically fails to capture the complete graph structure and the diverse relationships among multiple networks....	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	517,055
2302.06600	Task-Specific Skill Localization in Fine-tuned Language Models	Pre-trained language models can be fine-tuned to solve diverse NLP tasks, including in few-shot settings. Thus fine-tuning allows the model to quickly pick up task-specific ``skills,'' but there has been limited study of where these newly-learnt skills reside inside the massive model. This paper introduces the term ski...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	345,470
1204.2420	Variational Principle underlying Scale Invariant Social Systems	MaxEnt's variational principle, in conjunction with Shannon's logarithmic information measure, yields only exponential functional forms in straightforward fashion. In this communication we show how to overcome this limitation via the incorporation, into the variational process, of suitable dynamical information. As a c...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	15,409
2404.03606	Analyzing Musical Characteristics of National Anthems in Relation to Global Indices	Music plays a huge part in shaping peoples' psychology and behavioral patterns. This paper investigates the connection between national anthems and different global indices with computational music analysis and statistical correlation analysis. We analyze national anthem musical data to determine whether certain musica...	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	444,324
2107.06750	Fast and Slow Enigmas and Parental Guidance	We describe several additions to the ENIGMA system that guides clause selection in the E automated theorem prover. First, we significantly speed up its neural guidance by adding server-based GPU evaluation. The second addition is motivated by fast weight-based rejection filters that are currently used in systems like E...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	246,184
1507.01272	VEWS: A Wikipedia Vandal Early Warning System	We study the problem of detecting vandals on Wikipedia before any human or known vandalism detection system reports flagging potential vandals so that such users can be presented early to Wikipedia administrators. We leverage multiple classical ML approaches, but develop 3 novel sets of features. Our Wikipedia Vandal B...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	44,842
2111.07015	HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data Generation	Synthetic data generation overcomes limitations of real-world machine learning. Traditional methods are valuable for augmenting costly datasets but only optimize one criterion: realism. In this paper, we tackle the problem of generating synthetic data that optimize multiple criteria. This goal is necessary when real da...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	266,247
2101.00628	On Secure Degrees of Freedom of the MIMO Interference Channel with Local Output Feedback	This paper studies the problem of sum-secure degrees of freedom (SDoF) of the (M,M,N,N) multiple-input multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled vehicles-to-vehicles (V2V). For this...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	214,147
2111.00438	Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method	We discuss the problem of decentralized multi-agent reinforcement learning (MARL) in this work. In our setting, the global state, action, and reward are assumed to be fully observable, while the local policy is protected as privacy by each agent, and thus cannot be shared with others. There is a communication graph, am...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	264,220
2109.01942	On the ability of monolingual models to learn language-agnostic representations	Pretrained multilingual models have become a de facto default approach for zero-shot cross-lingual transfer. Previous work has shown that these models are able to achieve cross-lingual representations when pretrained on two or more languages with shared parameters. In this work, we provide evidence that a model can ach...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	253,585
1910.11986	Compensation of Charging Station Overload via On-road Mobile Energy Storage Scheduling	Supported by the technical development of electric battery and charging facilities, plug-in electric vehicle (PEV) has the potential to be mobile energy storage (MES) for energy delivery from resourceful charging stations (RCSs) to limited-capacity charging stations (LCSs). In this paper, we study the problem of using ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	150,928
2302.08669	Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories	Giving autonomous agents the ability to forecast their own outcomes and uncertainty will allow them to communicate their competencies and be used more safely. We accomplish this by using a learned world model of the agent system to forecast full agent trajectories over long time horizons. Real world systems involve sig...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	346,131
1907.05496	Online Learning to Estimate Warfarin Dose with Contextual Linear Bandits	Warfarin is one of the most commonly used oral blood anticoagulant agent in the world, the proper dose of Warfarin is difficult to establish not only because it is substantially variant among patients, but also adverse even severe consequences of taking an incorrect dose. Typical practice is to prescribe an initial dos...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,378
2408.00293	Gradient Flow Decoding	This paper presents the Gradient Flow (GF) decoding for LDPC codes. GF decoding, a continuous-time methodology based on gradient flow, employs a potential energy function associated with bipolar codewords of LDPC codes. The decoding process of the GF decoding is concisely defined by an ordinary differential equation an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	477,777
1906.08864	Accurate and Energy-Efficient Classification with Spiking Random Neural Network: Corrected and Expanded Version	Artificial Neural Network (ANN) based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	135,990
2109.12935	Time Series Model Attribution Visualizations as Explanations	Attributions are a common local explanation technique for deep learning models on single samples as they are easily extractable and demonstrate the relevance of input values. In many cases, heatmaps visualize such attributions for samples, for instance, on images. However, heatmaps are not always the ideal visualizatio...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	257,465
2402.05000	Pedagogical Alignment of Large Language Models	Large Language Models (LLMs), when used in educational settings without pedagogical fine-tuning, often provide immediate answers rather than guiding students through the problem-solving process. This approach falls short of pedagogically best practices and limits their effectiveness as educational tools. We term the ob...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	427,678
2205.07463	Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths	Implicit deep learning has recently become popular in the machine learning community since these implicit models can achieve competitive performance with state-of-the-art deep networks while using significantly less memory and computational resources. However, our theoretical understanding of when and how first-order m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,611
1812.00914	Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling	Knowledge distillation is an effective technique that transfers knowledge from a large teacher model to a shallow student. However, just like massive classification, large scale knowledge distillation also imposes heavy computational costs on training models of deep neural networks, as the softmax activations at the la...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	115,386
1608.05138	Hybrid CPU-GPU Framework for Network Motifs	Massively parallel architectures such as the GPU are becoming increasingly important due to the recent proliferation of data. In this paper, we propose a key class of hybrid parallel graphlet algorithms that leverages multiple CPUs and GPUs simultaneously for computing k-vertex induced subgraph statistics (called graph...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	59,931
1707.01047	Robust Optimization for Non-Convex Objectives	We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions. We develop a reduction from robust improper optimization to Bayesian optimization: given an oracle that returns $\alpha$-approximate solutions for distributions over objectives, we compute a di...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	76,459

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