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
2011.14251	Importance Weight Estimation and Generalization in Domain Adaptation under Label Shift	We study generalization under labeled shift for categorical and general normed label spaces. We propose a series of methods to estimate the importance weights from labeled source to unlabeled target domain and provide confidence bounds for these estimators. We deploy these estimators and provide generalization bounds i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,716
2005.14070	Exploiting Non-Linear Redundancy for Neural Model Compression	Deploying deep learning models, comprising of non-linear combination of millions, even billions, of parameters is challenging given the memory, power and compute constraints of the real world. This situation has led to research into model compression techniques most of which rely on suboptimal heuristics and do not con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,178
2001.01816	The Pedestrian Patterns Dataset	We present the pedestrian patterns dataset for autonomous driving. The dataset was collected by repeatedly traversing the same three routes for one week starting at different specific timeslots. The purpose of the dataset is to capture the patterns of social and pedestrian behavior along the traversed routes at differe...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	159,581
2411.15931	Improving Pre-Trained Self-Supervised Embeddings Through Effective Entropy Maximization	A number of different architectures and loss functions have been applied to the problem of self-supervised learning (SSL), with the goal of developing embeddings that provide the best possible pre-training for as-yet-unknown, lightly supervised downstream tasks. One of these SSL criteria is to maximize the entropy of a...	false	false	false	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	510,828
2003.05730	A Survey of Adversarial Learning on Graphs	Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, a line of studies has emerg...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	167,934
1811.04967	The Impact of Timestamp Granularity in Optimistic Concurrency Control	Optimistic concurrency control (OCC) can exploit the strengths of parallel hardware to provide excellent performance for uncontended transactions, and is popular in high-performance in-memory databases and transactional systems. But at high contention levels, OCC is susceptible to frequent aborts, leading to wasted wor...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	113,208
2305.12649	Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment	Source-free Unsupervised Domain Adaptation (SF-UDA) aims to adapt a well-trained source model to an unlabeled target domain without access to the source data. One key challenge is the lack of source data during domain adaptation. To handle this, we propose to mine the hidden knowledge of the source model and exploit it...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	366,108
2308.03995	Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks	The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GUs), holds significant promise for advancing smart city applications. However, resource management of the SAGIN is a challenge requiring urge...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	384,241
1601.01465	Maximum Leaf Spanning Trees of Growing Sierpinski Networks Models	The dynamical phenomena of complex networks are very difficult to predict from local information due to the rich microstructures and corresponding complex dynamics. On the other hands, it is a horrible job to compute some stochastic parameters of a large network having thousand and thousand nodes. We design several rec...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	50,751
2401.06178	AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks	Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impac...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	421,060
2305.11808	Pseudo-Label Training and Model Inertia in Neural Machine Translation	Like many other machine learning applications, neural machine translation (NMT) benefits from over-parameterized deep neural models. However, these models have been observed to be brittle: NMT model predictions are sensitive to small input changes and can show significant variation across re-training or incremental mod...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	365,703
2412.01936	Kernel-Free Universum Quadratic Surface Twin Support Vector Machines for Imbalanced Data	Binary classification tasks with imbalanced classes pose significant challenges in machine learning. Traditional classifiers often struggle to accurately capture the characteristics of the minority class, resulting in biased models with subpar predictive performance. In this paper, we introduce a novel approach to tack...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	513,312
2210.07402	The two-sided Galois duals of multi-twisted codes	Characterizing the duals of linear codes with rich algebraic structures received great interest in recent decades. The beginning was by representing cyclic codes over finite fields as ideals in the polynomial ring. Subsequently, studying the duals of constacyclic, quasi-cyclic, quasi-twisted, generalized quasi-cyclic, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	323,686
2412.09280	Learning to Solve Domain-Specific Calculation Problems with Knowledge-Intensive Programs Generator	Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs. But it still requires professional knowledge to facilitate the expertise for some domain-specific tasks. In this paper, we investigate into knowledge-intensive calculation problems. We find that the math problems to be ch...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	516,424
2107.06028	Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields	Dual decomposition approaches in nonconvex optimization may suffer from a duality gap. This poses a challenge when applying them directly to nonconvex problems such as MAP-inference in a Markov random field (MRF) with continuous state spaces. To eliminate such gaps, this paper considers a reformulation of the original ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,960
2402.00199	ViTacTip: Design and Verification of a Novel Biomimetic Physical Vision-Tactile Fusion Sensor	Tactile sensing is significant for robotics since it can obtain physical contact information during manipulation. To capture multimodal contact information within a compact framework, we designed a novel sensor called ViTacTip, which seamlessly integrates both tactile and visual perception capabilities into a single, i...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	425,515
2306.03174	Computational Design of Passive Grippers	This work proposes a novel generative design tool for passive grippers -- robot end effectors that have no additional actuation and instead leverage the existing degrees of freedom in a robotic arm to perform grasping tasks. Passive grippers are used because they offer interesting trade-offs between cost and capabiliti...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	371,216
2010.01245	Consensus Clustering With Unsupervised Representation Learning	Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must either be closer in the representation space, or have a similar cluster assignment. Bootstrap Your Own Latent (BYOL) is one such ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	198,575
2011.07509	Automated Intersection Management with MiniZinc	Ill-managed intersections are the primary reasons behind the increasing traffic problem in urban areas, leading to nonoptimal traffic-flow and unnecessary deadlocks. In this paper, we propose an automated intersection management system that extracts data from a well-defined grid of sensors and optimizes traffic flow by...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	206,584
1803.09539	On Matching Pursuit and Coordinate Descent	Two popular examples of first-order optimization methods over linear spaces are coordinate descent and matching pursuit algorithms, with their randomized variants. While the former targets the optimization by moving along coordinates, the latter considers a generalized notion of directions. Exploiting the connection be...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	93,518
2305.15720	Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors	Sparse annotation poses persistent challenges to training dense retrieval models; for example, it distorts the training signal when unlabeled relevant documents are used spuriously as negatives in contrastive learning. To alleviate this problem, we introduce evidence-based label smoothing, a novel, computationally effi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	367,742
2302.13463	Text-aided Group Decision-making Process Observation Method (x-GDP): A novel methodology for observing the joint decision-making process of travel choices	Joint travel decisions, particularly related to social activities remain poorly explained in traditional behavioral models. A key reason for this is the lack of empirical data, and the difficulties associated with collecting such data in the first place. To address this problem, we propose Text-aided Group Decision-mak...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	347,958
2301.05062	Tracr: Compiled Transformers as a Laboratory for Interpretability	We show how to "compile" human-readable programs into standard decoder-only transformer models. Our compiler, Tracr, generates models with known structure. This structure can be used to design experiments. For example, we use it to study "superposition" in transformers that execute multi-step algorithms. Additionally, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	340,246
2407.17013	Towards Indirect Data-Driven Predictive Control for Heating Phase of Thermoforming Process	Shaping thermoplastic sheets into three-dimensional products is challenging since overheating results in failed manufactured parts and wasted material. To this end, we propose an indirect data-driven predictive control approach using Model Predictive Control (MPC) capable of handling temperature constraints and heating...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	475,822
1303.3502	The Evolutionary Vaccination Dilemma in Complex Networks	In this work we analyze the evolution of voluntary vaccination in networked populations by entangling the spreading dynamics of an influenza-like disease with an evolutionary framework taking place at the end of each influenza season so that individuals take or not the vaccine upon their previous experience. Our framew...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	22,926
1711.08506	W-Net: A Deep Model for Fully Unsupervised Image Segmentation	While significant attention has been recently focused on designing supervised deep semantic segmentation algorithms for vision tasks, there are many domains in which sufficient supervised pixel-level labels are difficult to obtain. In this paper, we revisit the problem of purely unsupervised image segmentation and prop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,220
2112.14345	Reachability Analysis for FollowerStopper: Safety Analysis and Experimental Results	Motivated by earlier work and the developer of a new algorithm, the FollowerStopper, this article uses reachability analysis to verify the safety of the FollowerStopper algorithm, which is a controller designed for dampening stop- and-go traffic waves. With more than 1100 miles of driving data collected by our physical...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	273,502
2302.13183	On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds	Generative networks have experienced great empirical successes in distribution learning. Many existing experiments have demonstrated that generative networks can generate high-dimensional complex data from a low-dimensional easy-to-sample distribution. However, this phenomenon can not be justified by existing theories....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,849
2211.16421	RGB no more: Minimally-decoded JPEG Vision Transformers	Most neural networks for computer vision are designed to infer using RGB images. However, these RGB images are commonly encoded in JPEG before saving to disk; decoding them imposes an unavoidable overhead for RGB networks. Instead, our work focuses on training Vision Transformers (ViT) directly from the encoded feature...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	333,629
1901.09674	Deep Generative Graph Distribution Learning for Synthetic Power Grids	Power system studies require the topological structures of real-world power networks; however, such data is confidential due to important security concerns. Thus, power grid synthesis (PGS), i.e., creating realistic power grids that imitate actual power networks, has gained significant attention. In this letter, we cas...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,811
1602.02743	The IMP game: Learnability, approximability and adversarial learning beyond $\Sigma^0_1$	We introduce a problem set-up we call the Iterated Matching Pennies (IMP) game and show that it is a powerful framework for the study of three problems: adversarial learnability, conventional (i.e., non-adversarial) learnability and approximability. Using it, we are able to derive the following theorems. (1) It is poss...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	51,907
2410.19497	Available Degrees of Spatial Multiplexing of a Uniform Linear Array with Multiple Polarizations: A Holographic Perspective	The capabilities of multi-antenna technology have recently been significantly enhanced by the proliferation of extra large array architectures. The high dimensionality of these systems implies that communications take place in the nearfield regime, which poses some questions as to their effective perfomrance even under...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	502,343
2012.07887	Adaptive Verifiable Training Using Pairwise Class Similarity	Verifiable training has shown success in creating neural networks that are provably robust to a given amount of noise. However, despite only enforcing a single robustness criterion, its performance scales poorly with dataset complexity. On CIFAR10, a non-robust LeNet model has a 21.63% error rate, while a model created...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	211,594
2209.11222	Concept Activation Regions: A Generalized Framework For Concept-Based Explanations	Concept-based explanations permit to understand the predictions of a deep neural network (DNN) through the lens of concepts specified by users. Existing methods assume that the examples illustrating a concept are mapped in a fixed direction of the DNN's latent space. When this holds true, the concept can be represented...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	319,114
2107.08347	Beyond a binary of (non)racist tweets: A four-dimensional categorical detection and analysis of racist and xenophobic opinions on Twitter in early Covid-19	Transcending the binary categorization of racist and xenophobic texts, this research takes cues from social science theories to develop a four dimensional category for racism and xenophobia detection, namely stigmatization, offensiveness, blame, and exclusion. With the aid of deep learning techniques, this categorical ...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	246,697
2101.03418	Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies	Over the past decades, researchers have been pushing the limits of Deep Reinforcement Learning (DRL). Although DRL has attracted substantial interest from practitioners, many are blocked by having to search through a plethora of available methodologies that are seemingly alike, while others are still building RL agents...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,912
1910.10051	Unsupervised particle sorting for high-resolution single-particle cryo-EM	Single-particle cryo-Electron Microscopy (EM) has become a popular technique for determining the structure of challenging biomolecules that are inaccessible to other technologies. Recent advances in automation, both in data collection and data processing, have significantly lowered the barrier for non-expert users to s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	150,377
2206.11795	Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos	Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities. However, for many sequential decision domains such as robotics, video games, and computer use, publicly available data does not contain the l...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	304,372
2012.01701	FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques	It is extensively studied that Deep Neural Networks (DNNs) are vulnerable to Adversarial Examples (AEs). With more and more advanced adversarial attack methods have been developed, a quantity of corresponding defense solutions were designed to enhance the robustness of DNN models. It has become a popularity to leverage...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	209,500
1404.5683	The Likelihood Encoder for Lossy Source Compression	In this work, a likelihood encoder is studied in the context of lossy source compression. The analysis of the likelihood encoder is based on a soft-covering lemma. It is demonstrated that the use of a likelihood encoder together with the soft-covering lemma gives alternative achievability proofs for classical source co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,521
2110.14150	Training Wasserstein GANs without gradient penalties	We propose a stable method to train Wasserstein generative adversarial networks. In order to enhance stability, we consider two objective functions using the $c$-transform based on Kantorovich duality which arises in the theory of optimal transport. We experimentally show that this algorithm can effectively enforce the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	263,432
2107.04644	Self-Supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images	Dense depth estimation and 3D reconstruction of a surgical scene are crucial steps in computer assisted surgery. Recent work has shown that depth estimation from a stereo images pair could be solved with convolutional neural networks. However, most recent depth estimation models were trained on datasets with per-pixel ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,522
1212.0520	A modular framework for randomness extraction based on Trevisan's construction	Informally, an extractor delivers perfect randomness from a source that may be far away from the uniform distribution, yet contains some randomness. This task is a crucial ingredient of any attempt to produce perfectly random numbers---required, for instance, by cryptographic protocols, numerical simulations, or random...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	20,106
2305.16855	How opinions get more extreme in an age of information abundance	We live in an age of information abundance but know little about how this influences our opinions or attitudes. A common expectation is that people consulting numerous pieces of information, well balancing the different sides of an issue, will adopt a moderate attitude about the issue. We claim that this expectation is...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	368,289
2502.06136	Graph Neural Networks at a Fraction	Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data. In addition to real-valued GNNs, quaternion GNNs also perform well on tasks on graph-structured data. With the aim of reducing the energy footprint, we reduce the model size while maintaining accuracy comp...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	531,942
2404.15388	ML-based identification of the interface regions for coupling local and nonlocal models	Local-nonlocal coupling approaches combine the computational efficiency of local models and the accuracy of nonlocal models. However, the coupling process is challenging, requiring expertise to identify the interface between local and nonlocal regions. This study introduces a machine learning-based approach to automati...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	449,086
2009.06358	Improving Language Generation with Sentence Coherence Objective	Conditional story generation and contextual text continuation have become increasingly popular topics in NLP community. Existing models are often prone to output paragraphs of texts that gradually diverge from the given prompt. Although the generated text may have a reasonable perplexity and diversity, it could easily ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	195,609
2407.03045	JailbreakHunter: A Visual Analytics Approach for Jailbreak Prompts Discovery from Large-Scale Human-LLM Conversational Datasets	Large Language Models (LLMs) have gained significant attention but also raised concerns due to the risk of misuse. Jailbreak prompts, a popular type of adversarial attack towards LLMs, have appeared and constantly evolved to breach the safety protocols of LLMs. To address this issue, LLMs are regularly updated with saf...	true	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	470,001
2003.09984	Measurement-Level Fusion for OTHR Network Using Message Passing	Tracking an unknown number of targets based on multipath measurements provided by an over-the-horizon radar (OTHR) network with a statistical ionospheric model is complicated, which requires solving four subproblems: target detection, target tracking, multipath data association and ionospheric height identification. A ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	169,201
2305.04638	Learning Good Interventions in Causal Graphs via Covering	We study the causal bandit problem that entails identifying a near-optimal intervention from a specified set $A$ of (possibly non-atomic) interventions over a given causal graph. Here, an optimal intervention in ${A}$ is one that maximizes the expected value for a designated reward variable in the graph, and we use the...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	362,851
1511.01853	A Sparse Linear Model and Significance Test for Individual Consumption Prediction	Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well. Existing prediction methods usually have high relative errors that can be larger than 30% and have difficulties accounting...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	48,553
2101.04869	Convolutional Neural Nets in Chemical Engineering: Foundations, Computations, and Applications	In this paper we review the mathematical foundations of convolutional neural nets (CNNs) with the goals of: i) highlighting connections with techniques from statistics, signal processing, linear algebra, differential equations, and optimization, ii) demystifying underlying computations, and iii) identifying new types o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	215,265
2003.12611	Ontology Extraction and Usage in the Scholarly Knowledge Domain	Ontologies of research areas have been proven to be useful in many application for analysing and making sense of scholarly data. In this chapter, we present the Computer Science Ontology (CSO), which is the largest ontology of research areas in the field of Computer Science, and discuss a number of applications that bu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	169,955
1303.3320	On the preservation of commutation and anticommutation relations of N-level quantum systems	The goal of this paper is to provide conditions under which a quantum stochastic differential equation (QSDE) preserves the commutation and anticommutation relations of the SU(n) algebra, and thus describes the evolution of an open n-level quantum system. One of the challenges in the approach lies in the handling of th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	22,916
2304.08473	Solving Systems of Algebraic Equations Over Finite Commutative Rings and Applications	Several problems in algebraic geometry and coding theory over finite rings are modeled by systems of algebraic equations. Among these problems, we have the rank decoding problem, which is used in the construction of public-key cryptography. In 2004, Nechaev and Mikhailov proposed two methods for solving systems of poly...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	358,722
2501.18623	VLMaterial: Procedural Material Generation with Large Vision-Language Models	Procedural materials, represented as functional node graphs, are ubiquitous in computer graphics for photorealistic material appearance design. They allow users to perform intuitive and precise editing to achieve desired visual appearances. However, creating a procedural material given an input image requires professio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	528,780
2309.11600	Importance-aware Co-teaching for Offline Model-based Optimization	Offline model-based optimization aims to find a design that maximizes a property of interest using only an offline dataset, with applications in robot, protein, and molecule design, among others. A prevalent approach is gradient ascent, where a proxy model is trained on the offline dataset and then used to optimize the...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	393,463
2308.05081	Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling	Video Semantic Role Labeling (VidSRL) aims to detect the salient events from given videos, by recognizing the predict-argument event structures and the interrelationships between events. While recent endeavors have put forth methods for VidSRL, they can be mostly subject to two key drawbacks, including the lack of fine...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	384,677
1908.04018	An overlapping-free leaf segmentation method for plant point clouds	Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are so...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	141,388
1712.04185	Backpropagation generalized for output derivatives	Backpropagation algorithm is the cornerstone for neural network analysis. Paper extends it for training any derivatives of neural network's output with respect to its input. By the dint of it feedforward networks can be used to solve or verify solutions of partial or simple, linear or nonlinear differential equations. ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	86,570
2501.06940	Collaborative Human Activity Recognition with Passive Inter-Body Electrostatic Field	The passive body-area electrostatic field has recently been aspiringly explored for wearable motion sensing, harnessing its two thrilling characteristics: full-body motion sensitivity and environmental sensitivity, which potentially empowers human activity recognition both independently and jointly from a single sensin...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	524,197
2407.17417	Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?	Large Language Models (LLMs) have demonstrated impressive capabilities in generating diverse and contextually rich text. However, concerns regarding copyright infringement arise as LLMs may inadvertently produce copyrighted material. In this paper, we first investigate the effectiveness of watermarking LLMs as a deterr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	475,961
2010.07130	Exploiting Spectral Augmentation for Code-Switched Spoken Language Identification	Spoken language Identification (LID) systems are needed to identify the language(s) present in a given audio sample, and typically could be the first step in many speech processing related tasks such as automatic speech recognition (ASR). Automatic identification of the languages present in a speech signal is not only ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	200,719
2102.02914	MPC-Based Hierarchical Control of a Multi-Zone Commercial HVAC System	This paper presents a novel architecture for model predictive control (MPC) based indoor climate control of multi-zone buildings to provide energy efficiency. Unlike prior works we do not assume the availability of a high-resolution multi-zone building model, which is challenging to obtain. Instead, the architecture us...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	218,561
1912.00760	Temporarily Unavailable: Memory Inhibition in Cognitive and Computer Science	Inhibition is one of the core concepts in Cognitive Psychology. The idea of inhibitory mechanisms actively weakening representations in the human mind has inspired a great number of studies in various research domains. In contrast, Computer Science only recently has begun to consider inhibition as a second basic proces...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	155,882
2402.16611	Understanding the Dataset Practitioners Behind Large Language Model Development	As large language models (LLMs) become more advanced and impactful, it is increasingly important to scrutinize the data that they rely upon and produce. What is it to be a dataset practitioner doing this work? We approach this in two parts: first, we define the role of "dataset practitioners" by performing a retrospect...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	432,624
1109.3095	Convolutional Network Coding Based on Matrix Power Series Representation	In this paper, convolutional network coding is formulated by means of matrix power series representation of the local encoding kernel (LEK) matrices and global encoding kernel (GEK) matrices to establish its theoretical fundamentals for practical implementations. From the encoding perspective, the GEKs of a convolution...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	12,161
2402.06334	ExaRanker-Open: Synthetic Explanation for IR using Open-Source LLMs	ExaRanker recently introduced an approach to training information retrieval (IR) models, incorporating natural language explanations as additional labels. The method addresses the challenge of limited labeled examples, leading to improvements in the effectiveness of IR models. However, the initial results were based on...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	428,267
1206.6837	Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing	Inference for probabilistic graphical models is still very much a practical challenge in large domains. The commonly used and effective belief propagation (BP) algorithm and its generalizations often do not converge when applied to hard, real-life inference tasks. While it is widely recognized that the scheduling of me...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	17,065
2111.02351	Weight, Block or Unit? Exploring Sparsity Tradeoffs for Speech Enhancement on Tiny Neural Accelerators	We explore network sparsification strategies with the aim of compressing neural speech enhancement (SE) down to an optimal configuration for a new generation of low power microcontroller based neural accelerators (microNPU's). We examine three unique sparsity structures: weight pruning, block pruning and unit pruning; ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,842
2410.15280	Neural Normalized Compression Distance and the Disconnect Between Compression and Classification	It is generally well understood that predictive classification and compression are intrinsically related concepts in information theory. Indeed, many deep learning methods are explained as learning a kind of compression, and that better compression leads to better performance. We interrogate this hypothesis via the Nor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	500,457
2412.03736	Domain-specific Question Answering with Hybrid Search	Domain specific question answering is an evolving field that requires specialized solutions to address unique challenges. In this paper, we show that a hybrid approach combining a fine-tuned dense retriever with keyword based sparse search methods significantly enhances performance. Our system leverages a linear combin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	514,081
2011.06006	Towards NNGP-guided Neural Architecture Search	The predictions of wide Bayesian neural networks are described by a Gaussian process, known as the Neural Network Gaussian Process (NNGP). Analytic forms for NNGP kernels are known for many models, but computing the exact kernel for convolutional architectures is prohibitively expensive. One can obtain effective approx...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	206,106
1807.06391	Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game	Score following is the process of tracking a musical performance (audio) with respect to a known symbolic representation (a score). We start this paper by formulating score following as a multimodal Markov Decision Process, the mathematical foundation for sequential decision making. Given this formal definition, we add...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	103,111
2501.01477	A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design	Proteins are sequences of amino acids that serve as the basic building blocks of living organisms. Despite rapidly growing databases documenting structural and functional information for various protein sequences, our understanding of proteins remains limited because of the large possible sequence space and the complex...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	522,091
2310.17877	ASPIRO: Any-shot Structured Parsing-error-Induced ReprOmpting for Consistent Data-to-Text Generation	We present ASPIRO, an approach for structured data verbalisation into short template sentences in zero to few-shot settings. Unlike previous methods, our approach prompts large language models (LLMs) to directly produce entity-agnostic templates, rather than relying on LLMs to faithfully copy the given example entities...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	403,324
2406.14459	Healing Powers of BERT: How Task-Specific Fine-Tuning Recovers Corrupted Language Models	Language models like BERT excel at sentence classification tasks due to extensive pre-training on general data, but their robustness to parameter corruption is unexplored. To understand this better, we look at what happens if a language model is "broken", in the sense that some of its parameters are corrupted and then ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	466,318
1903.04646	An Open-Source 7-Axis, Robotic Platform to Enable Dexterous Procedures within CT Scanners	This paper describes the design, manufacture, and performance of a highly dexterous, low-profile, 7 Degree-of-Freedom (DOF) robotic arm for CT-guided percutaneous needle biopsy. Direct CT guidance allows physicians to localize tumours quickly; however, needle insertion is still performed by hand. This system is mounted...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	124,011
2406.13075	Exact Community Recovery (under Side Information): Optimality of Spectral Algorithms	In this paper, we study the problem of exact community recovery in general, two-community block models considering both Bernoulli and Gaussian matrix models, capturing the Stochastic Block Model, submatrix localization, and $\mathbb{Z}_2$-synchronization as special cases. We also study the settings where $side$ $inform...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	465,681
1810.09568	Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data	Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct offline performance and safety analyses. In this article, we develop a method for learning a probabilistic generative model of aircraft motion i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	111,079
2002.07448	Generating random bigraphs with preferential attachment	The bigraph theory is a relatively young, yet formally rigorous, mathematical framework encompassing Robin Milner's previous work on process calculi, on the one hand, and provides a generic meta-model for complex systems such as multi-agent systems, on the other. A bigraph $F = \langle F^P, F^L\rangle$ is a superpositi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	164,478
1605.06715	Factored Temporal Sigmoid Belief Networks for Sequence Learning	Deep conditional generative models are developed to simultaneously learn the temporal dependencies of multiple sequences. The model is designed by introducing a three-way weight tensor to capture the multiplicative interactions between side information and sequences. The proposed model builds on the Temporal Sigmoid Be...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	56,180
1611.05947	Minimal Problems for the Calibrated Trifocal Variety	We determine the algebraic degree of minimal problems for the calibrated trifocal variety in computer vision. We rely on numerical algebraic geometry and the homotopy continuation software Bertini.	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,095
2403.06063	Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialogue	Target-oriented proactive dialogue systems aim to lead conversations from a dialogue context toward a pre-determined target, such as making recommendations on designated items or introducing new specific topics. To this end, it is critical for such dialogue systems to plan reasonable actions to drive the conversation p...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	436,278
1811.12119	A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics	In this paper we propose a novel machine-learning method for anomaly detection applicable to data with periodic characteristics where randomly varying period lengths are explicitly allowed. A multi-dimensional time series analysis is conducted by training a data-adapted classifier consisting of deep convolutional neura...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,937
2209.05561	Optimising Fine-Grained Access Control Policy Enforcement for Database Queries. A Model-Driven Approach	Recently, we have proposed a model-driven approach for enforcing fine-grained access control (FGAC) policies when executing SQL queries. More concretely, we have defined a function SecQuery() that, given an FGAC policy S and a SQL select-statement q, generates a SQL stored-procedure SecQuery(S, q), such that: if a user...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	317,126
2303.02665	Heterogeneous Graph Learning for Acoustic Event Classification	Heterogeneous graphs provide a compact, efficient, and scalable way to model data involving multiple disparate modalities. This makes modeling audiovisual data using heterogeneous graphs an attractive option. However, graph structure does not appear naturally in audiovisual data. Graphs for audiovisual data are constru...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	349,444
2412.20110	Cross-Modal Mapping: Eliminating the Modality Gap for Few-Shot Image Classification	In few-shot image classification tasks, methods based on pretrained vision-language models (such as CLIP) have achieved significant progress. Many existing approaches directly utilize visual or textual features as class prototypes, however, these features fail to adequately represent their respective classes. We identi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	521,101
1412.5661	DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection	In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	38,510
2410.19627	Knowledge Graph Enhanced Language Agents for Recommendation	Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not understand the relationships between users and items, leading to inaccurate user profiles and ineffective recommendations. In this work, we explore...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	true	false	false	false	502,391
1901.07375	Extension of Convolutional Neural Network with General Image Processing Kernels	We applied pre-defined kernels also known as filters or masks developed for image processing to convolution neural network. Instead of letting neural networks find its own kernels, we used 41 different general-purpose kernels of blurring, edge detecting, sharpening, discrete cosine transformation, etc. for the first la...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	119,201
1908.07034	Symbiosis Promotes Fitness Improvements in the Game of Life	We present a computational simulation of evolving entities that includes symbiosis with shifting levels of selection. Evolution by natural selection shifts from the level of the original entities to the level of the new symbiotic entity. In the simulation, the fitness of an entity is measured by a series of one-on-one ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	142,185
2407.02499	Amortizing Pragmatic Program Synthesis with Rankings	The usage of Rational Speech Acts (RSA) framework has been successful in building \emph{pragmatic} program synthesizers that return programs which, in addition to being logically consistent with user-generated examples, account for the fact that a user chooses their examples informatively. We present a general method o...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	469,761
2305.09585	Inductive Graph Neural Networks for Moving Object Segmentation	Moving Object Segmentation (MOS) is a challenging problem in computer vision, particularly in scenarios with dynamic backgrounds, abrupt lighting changes, shadows, camouflage, and moving cameras. While graph-based methods have shown promising results in MOS, they have mainly relied on transductive learning which assume...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	364,693
2308.14776	Systematic reduction of Hyperspectral Images for high-throughput Plastic Characterization	Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess the spatial distribution of spectroscopically active compounds in objects, and has diverse applications in food quality control, pharmaceutical processes, and waste sorting. However, due to the large size of HSI datasets, it can be challenging t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	388,449
2407.15281	SynCPKL: Harnessing LLMs to Generate Synthetic Data for Commonsense Persona Knowledge Linking	Understanding rich dialogues often requires NLP systems to access relevant commonsense persona knowledge, but retrieving this knowledge is challenging due to complex contexts and the implicit nature of commonsense. This paper presents our approach to the Commonsense Persona Knowledge Linking (CPKL) challenge, addressin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	475,111
2207.09865	Discrete-Constrained Regression for Local Counting Models	Local counts, or the number of objects in a local area, is a continuous value by nature. Yet recent state-of-the-art methods show that formulating counting as a classification task performs better than regression. Through a series of experiments on carefully controlled synthetic data, we show that this counter-intuitiv...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,056
1903.11919	Imbalanced Sentiment Classification Enhanced with Discourse Marker	Imbalanced data commonly exists in real world, espacially in sentiment-related corpus, making it difficult to train a classifier to distinguish latent sentiment in text data. We observe that humans often express transitional emotion between two adjacent discourses with discourse markers like "but", "though", "while", e...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	125,608
1912.01597	Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates	We present two new remarkably simple stochastic second-order methods for minimizing the average of a very large number of sufficiently smooth and strongly convex functions. The first is a stochastic variant of Newton's method (SN), and the second is a stochastic variant of cubically regularized Newton's method (SCN). W...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	156,127
1904.10146	Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification	Graph Convolutional Neural Networks (GCNNs) are generalizations of CNNs to graph-structured data, in which convolution is guided by the graph topology. In many cases where graphs are unavailable, existing methods manually construct graphs or learn task-driven adaptive graphs. In this paper, we propose Graph Learning Ne...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	128,562

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