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
2204.07946	Integrated In-vehicle Monitoring System Using 3D Human Pose Estimation and Seat Belt Segmentation	Recently, along with interest in autonomous vehicles, the importance of monitoring systems for both drivers and passengers inside vehicles has been increasing. This paper proposes a novel in-vehicle monitoring system the combines 3D pose estimation, seat-belt segmentation, and seat-belt status classification networks. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	291,912
2012.07844	Decision-Making Algorithms for Learning and Adaptation with Application to COVID-19 Data	This work focuses on the development of a new family of decision-making algorithms for adaptation and learning, which are specifically tailored to decision problems and are constructed by building up on first principles from decision theory. A key observation is that estimation and decision problems are structurally di...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	211,591
2204.11700	ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching	Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching. However, current methods learn with complete graphs, resulting in a quadratic complexity in the number of features. Motivated by a prior observation that self- and cross- attention matrices converge to a spa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	293,236
1911.06557	Multi-Label Learning with Deep Forest	In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models. Deep neural network methods usually jointly embed the feature and label information into a latent space to exploit label correlations. However, the success of these me...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	153,573
2312.13136	Molecular Hypergraph Neural Networks	Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher-order connections like multi-center bonds and conjugated structures. To tackle this challeng...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	417,219
1610.05758	Uniform Recovery from Subgaussian Multi-Sensor Measurements	Parallel acquisition systems are employed successfully in a variety of different sensing applications when a single sensor cannot provide enough measurements for a high-quality reconstruction. In this paper, we consider compressed sensing (CS) for parallel acquisition systems when the individual sensors use subgaussian...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	62,559
2403.00863	LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction	Product attribute value extraction is a pivotal component in Natural Language Processing (NLP) and the contemporary e-commerce industry. The provision of precise product attribute values is fundamental in ensuring high-quality recommendations and enhancing customer satisfaction. The recently emerging Large Language Mod...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	434,159
2502.04895	Deep Learning Models for Physical Layer Communications	The increased availability of data and computing resources has enabled researchers to successfully adopt machine learning (ML) techniques and make significant contributions in several engineering areas. ML and in particular deep learning (DL) algorithms have shown to perform better in tasks where a physical bottom-up d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	531,358
1606.08660	Theory reconstruction: a representation learning view on predicate invention	With this positional paper we present a representation learning view on predicate invention. The intention of this proposal is to bridge the relational and deep learning communities on the problem of predicate invention. We propose a theory reconstruction approach, a formalism that extends autoencoder approach to repre...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	57,895
2305.11367	Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition	With the emphasis on healthcare, early childhood education, and fitness, non-invasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied because of its advantages of simple structure, easy access, visualization application, and harmlessness. This paper introd...	true	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	365,495
2403.08757	Efficient Combinatorial Optimization via Heat Diffusion	Combinatorial optimization problems are widespread but inherently challenging due to their discrete nature. The primary limitation of existing methods is that they can only access a small fraction of the solution space at each iteration, resulting in limited efficiency for searching the global optimal. To overcome this...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	437,455
2306.09980	Creating Multi-Level Skill Hierarchies in Reinforcement Learning	What is a useful skill hierarchy for an autonomous agent? We propose an answer based on a graphical representation of how the interaction between an agent and its environment may unfold. Our approach uses modularity maximisation as a central organising principle to expose the structure of the interaction graph at multi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	374,041
2012.07657	Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection	Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by unseen manipulation methods. Some recent works show improvements in generalisation but rely on cues that are easily corrupted by common post-processing operation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,540
2409.20434	QAEncoder: Towards Aligned Representation Learning in Question Answering System	Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses. However, the inherent gap between user queries and relevant documents hinders precise matching. Motivated by our conical distribution hypothesis, which posits that potential queries and documents form a cone-like struc...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	493,124
2009.07448	Question Directed Graph Attention Network for Numerical Reasoning over Text	Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation. To address this challenge, we propose a heterogeneous graph representation for the context of the pa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	195,928
2303.01510	INO at Factify 2: Structure Coherence based Multi-Modal Fact Verification	This paper describes our approach to the multi-modal fact verification (FACTIFY) challenge at AAAI2023. In recent years, with the widespread use of social media, fake news can spread rapidly and negatively impact social security. Automatic claim verification becomes more and more crucial to combat fake news. In fact ve...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	349,002
1911.04053	Decompressing Knowledge Graph Representations for Link Prediction	This paper studies the problem of predicting missing relationships between entities in knowledge graphs through learning their representations. Currently, the majority of existing link prediction models employ simple but intuitive scoring functions and relatively small embedding size so that they could be applied to la...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	152,871
2007.09370	How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning	This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens and local credibility to ensure fairness, in combination with differential privacy to guarantee privacy. In particular, we build a fair and d...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	187,920
1511.00799	A geometric approach to the dynamics of flapping wing micro aerial vehicles: Modelling and reduction	This paper presents a geometric framework for analysis of dynamics of flapping wing micro aerial vehicles (FWMAV) which achieve locomotion in the special Euclidean group SE(3) using internal shape changes. We review the special structure of the configuration manifold of such systems. This work addresses to extend the w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	48,444
1801.00606	Secrecy Capacity-Memory Tradeoff of Erasure Broadcast Channels	This paper derives upper and lower bounds on the secrecy capacity-memory tradeoff of a wiretap erasure broadcast channel (BC) with Kw weak receivers and Ks strong receivers, where weak receivers, respectively strong receivers, have same erasure probabilities and cache sizes. The lower bounds are achieved by schemes tha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	87,599
2304.00320	Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability	Random label noises (or observational noises) widely exist in practical machine learning settings. While previous studies primarily focus on the affects of label noises to the performance of learning, our work intends to investigate the implicit regularization effects of the label noises, under mini-batch sampling sett...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	355,635
2304.06790	Inpaint Anything: Segment Anything Meets Image Inpainting	Modern image inpainting systems, despite the significant progress, often struggle with mask selection and holes filling. Based on Segment-Anything Model (SAM), we make the first attempt to the mask-free image inpainting and propose a new paradigm of ``clicking and filling'', which is named as Inpaint Anything (IA). The...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	358,101
2407.05287	Model-agnostic meta-learners for estimating heterogeneous treatment effects over time	Estimating heterogeneous treatment effects (HTEs) over time is crucial in many disciplines such as personalized medicine. For example, electronic health records are commonly collected over several time periods and then used to personalize treatment decisions. Existing works for this task have mostly focused on model-ba...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	470,908
2308.14250	Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification	Classification of movement trajectories has many applications in transportation and is a key component for large-scale movement trajectory generation and anomaly detection which has key safety applications in the aftermath of a disaster or other external shock. However, the current state-of-the-art (SOTA) are based on ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	388,248
1104.3911	Information Exchange Limits in Cooperative MIMO Networks	Concurrent presence of inter-cell and intra-cell interferences constitutes a major impediment to reliable downlink transmission in multi-cell multiuser networks. Harnessing such interferences largely hinges on two levels of information exchange in the network: one from the users to the base-stations (feedback) and the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	10,047
1602.03966	Measuring and Maximizing Influence via Random Walk in Social Activity Networks	With the popularity of OSNs, finding a set of most influential users (or nodes) so as to trigger the largest influence cascade is of significance. For example, companies may take advantage of the "word-of-mouth" effect to trigger a large cascade of purchases by offering free samples/discounts to those most influential ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	52,073
1904.03654	Optimal control of batch processes via a deterministic Q-learning method	Dynamic optimization of nonlinear chemical systems -- such as batch reactors -- should be applied online, and the suitable control taken should be according to the current state of the system rather than the current time instant. The recent state of the art methods applies the control based on the current time instant ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	126,803
2110.14748	Context-Tree-Based Lossy Compression and Its Application to CSI Representation	We propose novel compression algorithms for time-varying channel state information (CSI) in wireless communications. The proposed scheme combines (lossy) vector quantisation and (lossless) compression. First, the new vector quantisation technique is based on a class of parametrised companders applied on each component ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	263,619
2005.10904	Solving a steady-state PDE using spiking networks and neuromorphic hardware	The widely parallel, spiking neural networks of neuromorphic processors can enable computationally powerful formulations. While recent interest has focused on primarily machine learning tasks, the space of appropriate applications is wide and continually expanding. Here, we leverage the parallel and event-driven struct...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	178,316
2312.12608	Rethinking Randomized Smoothing from the Perspective of Scalability	Machine learning models have demonstrated remarkable success across diverse domains but remain vulnerable to adversarial attacks. Empirical defense mechanisms often fail, as new attacks constantly emerge, rendering existing defenses obsolete, shifting the focus to certification-based defenses. Randomized smoothing has ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	417,020
2211.03074	A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization	Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential comm...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	328,825
2103.14943	HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset	High dynamic range (HDR) video reconstruction from sequences captured with alternating exposures is a very challenging problem. Existing methods often align low dynamic range (LDR) input sequence in the image space using optical flow, and then merge the aligned images to produce HDR output. However, accurate alignment ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	227,024
2408.12588	Real-Time Video Generation with Pyramid Attention Broadcast	We present Pyramid Attention Broadcast (PAB), a real-time, high quality and training-free approach for DiT-based video generation. Our method is founded on the observation that attention difference in the diffusion process exhibits a U-shaped pattern, indicating significant redundancy. We mitigate this by broadcasting ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	482,791
1805.06042	Automated Vision-based Bridge Component Extraction Using Multiscale Convolutional Neural Networks	Image data has a great potential of helping post-earthquake visual inspections of civil engineering structures due to the ease of data acquisition and the advantages in capturing visual information. A variety of techniques have been applied to detect damages automatically from a close-up image of a structural component...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	97,519
2411.07152	HierTOD: A Task-Oriented Dialogue System Driven by Hierarchical Goals	Task-Oriented Dialogue (TOD) systems assist users in completing tasks through natural language interactions, often relying on a single-layered workflow structure for slot-filling in public tasks, such as hotel bookings. However, in enterprise environments, which involve rich domain-specific knowledge, TOD systems face ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	507,414
2106.03337	Summary Grounded Conversation Generation	Many conversation datasets have been constructed in the recent years using crowdsourcing. However, the data collection process can be time consuming and presents many challenges to ensure data quality. Since language generation has improved immensely in recent years with the advancement of pre-trained language models, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	239,276
2004.13999	Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework	As the modern world becomes increasingly digitized and interconnected, distributed signal processing has proven to be effective in processing its large volume of data. However, a main challenge limiting the broad use of distributed signal processing techniques is the issue of privacy in handling sensitive data. To addr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	174,746
1701.03891	Learning to Invert: Signal Recovery via Deep Convolutional Networks	The promise of compressive sensing (CS) has been offset by two significant challenges. First, real-world data is not exactly sparse in a fixed basis. Second, current high-performance recovery algorithms are slow to converge, which limits CS to either non-real-time applications or scenarios where massive back-end comput...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	66,771
2111.08274	HADFL: Heterogeneity-aware Decentralized Federated Learning Framework	Federated learning (FL) supports training models on geographically distributed devices. However, traditional FL systems adopt a centralized synchronous strategy, putting high communication pressure and model generalization challenge. Existing optimizations on FL either fail to speedup training on heterogeneous devices ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	266,629
2102.10075	Sentiment Analysis for YouTube Comments in Roman Urdu	Sentiment analysis is a vast area in the Machine learning domain. A lot of work is done on datasets and their analysis of the English Language. In Pakistan, a huge amount of data is in roman Urdu language, it is scattered all over the social sites including Twitter, YouTube, Facebook and similar applications. In this s...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	220,970
1807.06814	Determining ellipses from low-resolution images with a comprehensive image formation model	When determining the parameters of a parametric planar shape based on a single low-resolution image, common estimation paradigms lead to inaccurate parameter estimates. The reason behind poor estimation results is that standard estimation frameworks fail to model the image formation process at a sufficiently detailed l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,211
1804.07873	3D Human Pose Estimation on a Configurable Bed from a Pressure Image	Robots have the potential to assist people in bed, such as in healthcare settings, yet bedding materials like sheets and blankets can make observation of the human body difficult for robots. A pressure-sensing mat on a bed can provide pressure images that are relatively insensitive to bedding materials. However, prior ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	95,623
2109.00062	Shallow pooling for sparse labels	Recent years have seen enormous gains in core IR tasks, including document and passage ranking. Datasets and leaderboards, and in particular the MS MARCO datasets, illustrate the dramatic improvements achieved by modern neural rankers. When compared with traditional test collections, the MS MARCO datasets employ substa...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	252,987
2403.04562	Out of the Room: Generalizing Event-Based Dynamic Motion Segmentation for Complex Scenes	Rapid and reliable identification of dynamic scene parts, also known as motion segmentation, is a key challenge for mobile sensors. Contemporary RGB camera-based methods rely on modeling camera and scene properties however, are often under-constrained and fall short in unknown categories. Event cameras have the potenti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	435,644
2305.10300	One-Prompt to Segment All Medical Images	Large foundation models, known for their strong zero-shot generalization, have excelled in visual and language applications. However, applying them to medical image segmentation, a domain with diverse imaging types and target labels, remains an open challenge. Current approaches, such as adapting interactive segmentati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	364,993
1803.04868	Search-based optimal motion planning for automated driving	This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenie...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	92,534
1703.08238	Semi-Automatic Segmentation and Ultrasonic Characterization of Solid Breast Lesions	Characterization of breast lesions is an essential prerequisite to detect breast cancer in an early stage. Automatic segmentation makes this categorization method robust by freeing it from subjectivity and human error. Both spectral and morphometric features are successfully used for differentiating between benign and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,542
2212.01287	SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection	Change detection (CD) aims to find the difference between two images at different times and outputs a change map to represent whether the region has changed or not. To achieve a better result in generating the change map, many State-of-The-Art (SoTA) methods design a deep learning model that has a powerful discriminati...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	334,374
1611.07571	Quad-networks: unsupervised learning to rank for interest point detection	Several machine learning tasks require to represent the data using only a sparse set of interest points. An ideal detector is able to find the corresponding interest points even if the data undergo a transformation typical for a given domain. Since the task is of high practical interest in computer vision, many hand-cr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	64,367
2202.08426	Synthetic Control As Online Linear Regression	This paper notes a simple connection between synthetic control and online learning. Specifically, we recognize synthetic control as an instance of Follow-The-Leader (FTL). Standard results in online convex optimization then imply that, even when outcomes are chosen by an adversary, synthetic control predictions of coun...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	280,869
2403.17552	Naive Bayes-based Context Extension for Large Language Models	Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonst...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	441,518
1312.0286	Efficient Learning and Planning with Compressed Predictive States	Predictive state representations (PSRs) offer an expressive framework for modelling partially observable systems. By compactly representing systems as functions of observable quantities, the PSR learning approach avoids using local-minima prone expectation-maximization and instead employs a globally optimal moment-base...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	28,776
2208.07669	On Optimizing Back-Substitution Methods for Neural Network Verification	With the increasing application of deep learning in mission-critical systems, there is a growing need to obtain formal guarantees about the behaviors of neural networks. Indeed, many approaches for verifying neural networks have been recently proposed, but these generally struggle with limited scalability or insufficie...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	313,114
2110.12006	Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator	High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology. In order to improve the day-to-dayoperations and maximize the delivery of the science, new analytical techniques are being exploredfor anomaly detection, classificati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,677
2501.17332	Compact Neural TTS Voices for Accessibility	Contemporary text-to-speech solutions for accessibility applications can typically be classified into two categories: (i) device-based statistical parametric speech synthesis (SPSS) or unit selection (USEL) and (ii) cloud-based neural TTS. SPSS and USEL offer low latency and low disk footprint at the expense of natural...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	528,296
2309.10837	Improving Opioid Use Disorder Risk Modelling through Behavioral and Genetic Feature Integration	Opioids are an effective analgesic for acute and chronic pain, but also carry a considerable risk of addiction leading to millions of opioid use disorder (OUD) cases and tens of thousands of premature deaths in the United States yearly. Estimating OUD risk prior to prescription could improve the efficacy of treatment r...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	393,176
1507.03839	Transmit Power Minimization in Small Cell Networks Under Time Average QoS Constraints	We consider a small cell network (SCN) consisting of N cells, with the small cell base stations (SCBSs) equipped with Nt \geq 1 antennas each, serving K single antenna user terminals (UTs) per cell. Under this set up, we address the following question: given certain time average quality of service (QoS) targets for the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,110
2408.08454	Beyond Uniform Query Distribution: Key-Driven Grouped Query Attention	The Transformer architecture has revolutionized deep learning through its Self-Attention mechanism, which effectively captures contextual information. However, the memory footprint of Self-Attention presents significant challenges for long-sequence tasks. Grouped Query Attention (GQA) addresses this issue by grouping q...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	480,998
2403.18407	A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification	Semi-supervised learning (SSL) is a practical challenge in computer vision. Pseudo-label (PL) methods, e.g., FixMatch and FreeMatch, obtain the State Of The Art (SOTA) performances in SSL. These approaches employ a threshold-to-pseudo-label (T2L) process to generate PLs by truncating the confidence scores of unlabeled ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	441,926
1904.13240	Generating protein sequences from antibiotic resistance genes data using Generative Adversarial Networks	We introduce a method to generate synthetic protein sequences which are predicted to be resistant to certain antibiotics. We did this using 6,023 genes that were predicted to be resistant to antibiotics in the intestinal region of the human gut and were fed as input to a Wasserstein generative adversarial network (W-GA...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	129,335
2404.08111	S3Editor: A Sparse Semantic-Disentangled Self-Training Framework for Face Video Editing	Face attribute editing plays a pivotal role in various applications. However, existing methods encounter challenges in achieving high-quality results while preserving identity, editing faithfulness, and temporal consistency. These challenges are rooted in issues related to the training pipeline, including limited super...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	446,116
2007.10040	Knowledge Graph Extraction from Videos	Nearly all existing techniques for automated video annotation (or captioning) describe videos using natural language sentences. However, this has several shortcomings: (i) it is very hard to then further use the generated natural language annotations in automated data processing, (ii) generating natural language annota...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	188,151
2311.16496	Can Out-of-Domain data help to Learn Domain-Specific Prompts for Multimodal Misinformation Detection?	Spread of fake news using out-of-context images and captions has become widespread in this era of information overload. Since fake news can belong to different domains like politics, sports, etc. with their unique characteristics, inference on a test image-caption pair is contingent on how well the model has been train...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	410,921
2409.03794	Evaluating Machine Learning-based Skin Cancer Diagnosis	This study evaluates the reliability of two deep learning models for skin cancer detection, focusing on their explainability and fairness. Using the HAM10000 dataset of dermatoscopic images, the research assesses two convolutional neural network architectures: a MobileNet-based model and a custom CNN model. Both models...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	486,181
1710.05700	Performance Guaranteed Inertia Emulation for Diesel-Wind System Feed Microgrid via Model Reference Control	In this paper, a model reference control based inertia emulation strategy is proposed. Desired inertia can be precisely emulated through this control strategy so that guaranteed performance is ensured. A typical frequency response model with parametrical inertia is set to be the reference model. A measurement at a spec...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	82,667
1902.08452	Nonconvex sampling with the Metropolis-adjusted Langevin algorithm	The Langevin Markov chain algorithms are widely deployed methods to sample from distributions in challenging high-dimensional and non-convex statistics and machine learning applications. Despite this, current bounds for the Langevin algorithms are slower than those of competing algorithms in many important situations, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	122,194
1805.11712	A Novel Multi-clustering Method for Hierarchical Clusterings, Based on Boosting	Bagging and boosting are proved to be the best methods of building multiple classifiers in classification combination problems. In the area of "flat clustering" problems, it is also recognized that multi-clustering methods based on boosting provide clusterings of an improved quality. In this paper, we introduce a novel...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	98,983
1905.02704	A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks	In this era of machine learning models, their functionality is being threatened by adversarial attacks. In the face of this struggle for making artificial neural networks robust, finding a model, resilient to these attacks, is very important. In this work, we present, for the first time, a comprehensive analysis of the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	130,030
2007.09878	Frustratingly Hard Evidence Retrieval for QA Over Books	A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth. We formulate BookQA as an open-domain QA task given its similar dependency on evidence retrieval. We further investigate how state-of-the-art open...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	188,096
2407.13609	Training-free Composite Scene Generation for Layout-to-Image Synthesis	Recent breakthroughs in text-to-image diffusion models have significantly advanced the generation of high-fidelity, photo-realistic images from textual descriptions. Yet, these models often struggle with interpreting spatial arrangements from text, hindering their ability to produce images with precise spatial configur...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	474,442
2403.08062	Efficient Fault Tolerance for Pipelined Query Engines via Write-ahead Lineage	Modern distributed pipelined query engines either do not support intra-query fault tolerance or employ high-overhead approaches such as persisting intermediate outputs or checkpointing state. In this work, we present write-ahead lineage, a novel fault recovery technique that combines Spark's lineage-based replay and wr...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	437,161
2208.12232	A survey, review, and future trends of skin lesion segmentation and classification	The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a u...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	314,664
2412.17748	Aerial Assistive Payload Transportation Using Quadrotor UAVs with Nonsingular Fast Terminal SMC for Human Physical Interaction	This paper presents a novel approach to utilizing underactuated quadrotor Unmanned Aerial Vehicles (UAVs) as assistive devices in cooperative payload transportation task through human guidance and physical interaction. The proposed system consists of two underactuated UAVs rigidly connected to the transported payload. ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	520,100
cmp-lg/9808009	How to define a context-free backbone for DGs: Implementing a DG in the LFG formalism	This paper presents a multidimensional Dependency Grammar (DG), which decouples the dependency tree from word order, such that surface ordering is not determined by traversing the dependency tree. We develop the notion of a \emph{word order domain structure}, which is linked but structurally dissimilar to the syntactic...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,916
2001.09637	Structural Information Learning Machinery: Learning from Observing, Associating, Optimizing, Decoding, and Abstracting	In the present paper, we propose the model of {\it structural information learning machines} (SiLeM for short), leading to a mathematical definition of learning by merging the theories of computation and information. Our model shows that the essence of learning is {\it to gain information}, that to gain information is ...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	161,630
2410.06842	SurANet: Surrounding-Aware Network for Concealed Object Detection via Highly-Efficient Interactive Contrastive Learning Strategy	Concealed object detection (COD) in cluttered scenes is significant for various image processing applications. However, due to that concealed objects are always similar to their background, it is extremely hard to distinguish them. Here, the major obstacle is the tiny feature differences between the inside and outside ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	496,368
1309.2675	A Brief Study of Open Source Graph Databases	With the proliferation of large irregular sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis tech...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	26,965
2301.09708	Koopman Operators for Modeling and Control of Soft Robotics	Purpose of review: We review recent advances in algorithmic development and validation for modeling and control of soft robots leveraging the Koopman operator theory. Recent findings: We identify the following trends in recent research efforts in this area. (1) The design of lifting functions used in the data-driven ap...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	341,574
2308.10089	Root Pose Decomposition Towards Generic Non-rigid 3D Reconstruction with Monocular Videos	This work focuses on the 3D reconstruction of non-rigid objects based on monocular RGB video sequences. Concretely, we aim at building high-fidelity models for generic object categories and casually captured scenes. To this end, we do not assume known root poses of objects, and do not utilize category-specific template...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,564
2102.06665	Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction	Recent deep learning approaches focus on improving quantitative scores of dedicated benchmarks, and therefore only reduce the observation-related (aleatoric) uncertainty. However, the model-immanent (epistemic) uncertainty is less frequently systematically analyzed. In this work, we introduce a Bayesian variational fra...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	219,832
2406.06634	Sparse Binarization for Fast Keyword Spotting	With the increasing prevalence of voice-activated devices and applications, keyword spotting (KWS) models enable users to interact with technology hands-free, enhancing convenience and accessibility in various contexts. Deploying KWS models on edge devices, such as smartphones and embedded systems, offers significant b...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	462,711
2307.05473	Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives	Given a set of calibrated images of a scene, we present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. While many approaches focus on recovering high-fidelity 3D scenes, we focus on parsing a scene into mid-level 3D representations made of a small set of t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	378,753
1603.06220	Flow of Information in Feed-Forward Deep Neural Networks	Feed-forward deep neural networks have been used extensively in various machine learning applications. Developing a precise understanding of the underling behavior of neural networks is crucial for their efficient deployment. In this paper, we use an information theoretic approach to study the flow of information in a ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	53,467
1511.01640	Computing sets of graded attribute implications with witnessed non-redundancy	In this paper we extend our previous results on sets of graded attribute implications with witnessed non-redundancy. We assume finite residuated lattices as structures of truth degrees and use arbitrary idempotent truth-stressing linguistic hedges as parameters which influence the semantics of graded attribute implicat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	48,526
2210.08050	Multi-trainer Interactive Reinforcement Learning System	Interactive reinforcement learning can effectively facilitate the agent training via human feedback. However, such methods often require the human teacher to know what is the correct action that the agent should take. In other words, if the human teacher is not always reliable, then it will not be consistently able to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	323,960
2312.00485	Backbone-based Dynamic Graph Spatio-Temporal Network for Epidemic Forecasting	Accurate epidemic forecasting is a critical task in controlling disease transmission. Many deep learning-based models focus only on static or dynamic graphs when constructing spatial information, ignoring their relationship. Additionally, these models often rely on recurrent structures, which can lead to error accumula...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	412,067
1908.03875	Inference of Edge Correlations in Multilayer Networks	Many recent developments in network analysis have focused on multilayer networks, which one can use to encode time-dependent interactions, multiple types of interactions, and other complications that arise in complex systems. Like their monolayer counterparts, multilayer networks in applications often have mesoscale fe...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	141,346
1711.05680	An Unsupervised Approach for Mapping between Vector Spaces	We present a language independent, unsupervised approach for transforming word embeddings from source language to target language using a transformation matrix. Our model handles the problem of data scarcity which is faced by many languages in the world and yields improved word embeddings for words in the target langua...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	84,614
2402.08819	Infinite-horizon optimal scheduling for feedback control	Emerging cyber-physical systems impel the development of communication protocols that optimize resource utilization. This article investigates infinite-horizon optimal scheduling for resource-aware networked control systems by addressing the rate-regulation tradeoff. Consider a scenario where the sensor and the control...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	429,252
2306.15176	Evaluation and Optimization of Rendering Techniques for Autonomous Driving Simulation	In order to meet the demand for higher scene rendering quality from some autonomous driving teams (such as those focused on CV), we have decided to use an offline simulation industrial rendering framework instead of real-time rendering in our autonomous driving simulator. Our plan is to generate lower-quality scenes us...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	375,939
2305.05882	Deep Partial Multi-Label Learning with Graph Disambiguation	In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels. Recently, graph-based methods, which demonstrate a good ability to estimate accurate confidence scores from candidate labels, have been prevale...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	363,321
2307.01098	Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis	This study aims to develop a new computational pathology approach that automates the identification and quantification of myocardial inflammatory infiltration in digital HE-stained images to provide a quantitative histological diagnosis of myocarditis.898 HE-stained whole slide images (WSIs) of myocardium from 154 hear...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	377,236
2203.12054	Self-supervision through Random Segments with Autoregressive Coding (RandSAC)	Inspired by the success of self-supervised autoregressive representation learning in natural language (GPT and its variants), and advances in recent visual architecture design with Vision Transformers (ViTs), in this paper, we explore the effect various design choices have on the success of applying such training strat...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	287,118
2310.19704	A Survey on Knowledge Editing of Neural Networks	Deep neural networks are becoming increasingly pervasive in academia and industry, matching and surpassing human performance on a wide variety of fields and related tasks. However, just as humans, even the largest artificial neural networks make mistakes, and once-correct predictions can become invalid as the world pro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	404,100
1907.06795	Efficient Autonomy Validation in Simulation with Adaptive Stress Testing	During the development of autonomous systems such as driverless cars, it is important to characterize the scenarios that are most likely to result in failure. Adaptive Stress Testing (AST) provides a way to search for the most-likely failure scenario as a Markov decision process (MDP). Our previous work used a deep rei...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	true	138,703
0812.0319	Secrecy Capacity of a Class of Broadcast Channels with an Eavesdropper	We study the security of communication between a single transmitter and multiple receivers in a broadcast channel in the presence of an eavesdropper. We consider several special classes of channels. As the first model, we consider the degraded multi-receiver wiretap channel where the legitimate receivers exhibit a degr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,728
1606.00575	Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks	Parallelization framework has become a necessity to speed up the training of deep neural networks (DNN) recently. Such framework typically employs the Model Average approach, denoted as MA-DNN, in which parallel workers conduct respective training based on their own local data while the parameters of local models are p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	56,682
2204.01457	SHiFT: An Efficient, Flexible Search Engine for Transfer Learning	Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch. The emergence of rich model repositories, such as TensorFlow Hub, enables practitioners and researchers to unleash the potential of these models across a wide range of downstream tasks. As these repositories keep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	289,606
2405.17374	Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models	Safety alignment is crucial to ensure that large language models (LLMs) behave in ways that align with human preferences and prevent harmful actions during inference. However, recent studies show that the alignment can be easily compromised through finetuning with only a few adversarially designed training examples. We...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	457,876
1807.09886	Evaluating Creativity in Computational Co-Creative Systems	This paper provides a framework for evaluating creativity in co-creative systems: those that involve computer programs collaborating with human users on creative tasks. We situate co-creative systems within a broader context of computational creativity and explain the unique qualities of these systems. We present four ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	103,813

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