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
1707.06066	Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding	The celebrated sparse representation model has led to remarkable results in various signal processing tasks in the last decade. However, despite its initial purpose of serving as a global prior for entire signals, it has been commonly used for modeling low dimensional patches due to the computational constraints it ent...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	77,347
2208.09201	Improving Post-Processing of Audio Event Detectors Using Reinforcement Learning	We apply post-processing to the class probability distribution outputs of audio event classification models and employ reinforcement learning to jointly discover the optimal parameters for various stages of a post-processing stack, such as the classification thresholds and the kernel sizes of median filtering algorithm...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	313,625
2502.01074	Omni-Mol: Exploring Universal Convergent Space for Omni-Molecular Tasks	Building generalist models has recently demonstrated remarkable capabilities in diverse scientific domains. Within the realm of molecular learning, several studies have explored unifying diverse tasks across diverse domains. However, negative conflicts and interference between molecules and knowledge from different dom...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	529,681
2407.19594	Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge	Large Language Models (LLMs) are rapidly surpassing human knowledge in many domains. While improving these models traditionally relies on costly human data, recent self-rewarding mechanisms (Yuan et al., 2024) have shown that LLMs can improve by judging their own responses instead of relying on human labelers. However,...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	476,848
1601.04724	Interference Alignment in MIMO Interference Channels using SDP Relaxation	Nowadays, providing higher data rate is a momentous goal for wireless communications systems. Interference is one of the important obstacles to reach this purpose. Interference alignment is a management technique that align interference from other transmitters in the least possible dimension subspace at each receiver a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	51,052
2409.08277	Depth on Demand: Streaming Dense Depth from a Low Frame Rate Active Sensor	High frame rate and accurate depth estimation plays an important role in several tasks crucial to robotics and automotive perception. To date, this can be achieved through ToF and LiDAR devices for indoor and outdoor applications, respectively. However, their applicability is limited by low frame rate, energy consumpti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	487,843
2308.05870	UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data	To satisfy the broad applications and insatiable hunger for deploying low latency multimedia data classification and data privacy in a cloud-based setting, federated learning (FL) has emerged as an important learning paradigm. For the practical cases involving limited computational power and only unlabeled data in many...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	384,934
1906.02702	A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent	This paper is concerned with minimizing the average of $n$ cost functions over a network in which agents may communicate and exchange information with each other. We consider the setting where only noisy gradient information is available. To solve the problem, we study the distributed stochastic gradient descent (DSGD)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	134,150
1702.07203	Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT	We investigate pivot-based translation between related languages in a low resource, phrase-based SMT setting. We show that a subword-level pivot-based SMT model using a related pivot language is substantially better than word and morpheme-level pivot models. It is also highly competitive with the best direct translatio...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	68,745
2309.09336	Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for Rainfall Prediction in North-East India	Accurate rainfall forecasting is crucial for effective disaster preparedness and mitigation in the North-East region of India, which is prone to extreme weather events such as floods and landslides. In this study, we investigated the use of two data-driven methods, Dynamic Mode Decomposition (DMD) and Long Short-Term M...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	392,568
1009.0571	Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization	Relative to the large literature on upper bounds on complexity of convex optimization, lesser attention has been paid to the fundamental hardness of these problems. Given the extensive use of convex optimization in machine learning and statistics, gaining an understanding of these complexity-theoretic issues is importa...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	7,466
2107.09225	Discriminator-Free Generative Adversarial Attack	The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images. Most of the existing works for adversarial attack are gradient-based and suf-fer from the latency efficiencies and the load on GPU memory. Thegenerat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	246,968
2007.15576	Dense Scene Multiple Object Tracking with Box-Plane Matching	Multiple Object Tracking (MOT) is an important task in computer vision. MOT is still challenging due to the occlusion problem, especially in dense scenes. Following the tracking-by-detection framework, we propose the Box-Plane Matching (BPM) method to improve the MOT performacne in dense scenes. First, we design the La...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,702
2011.08575	Audience Creation for Consumables -- Simple and Scalable Precision Merchandising for a Growing Marketplace	Consumable categories, such as grocery and fast-moving consumer goods, are quintessential to the growth of e-commerce marketplaces in developing countries. In this work, we present the design and implementation of a precision merchandising system, which creates audience sets from over 10 million consumers and is deploy...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	206,924
2210.13432	Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models	Large language models (LLM) trained using the next-token-prediction objective, such as GPT3 and PaLM, have revolutionized natural language processing in recent years by showing impressive zero-shot and few-shot capabilities across a wide range of tasks. In this work, we propose a simple technique that significantly boo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	326,158
1801.00121	Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues	This article presents advances in resource allocation (RA) for downlink non-orthogonal multiple access (NOMA) systems, focusing on user pairing (UP) and power allocation (PA) algorithms. The former pairs the users to obtain the high capacity gain by exploiting the channel gain difference between the users, while the la...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	87,513
2104.02000	Can audio-visual integration strengthen robustness under multimodal attacks?	In this paper, we propose to make a systematic study on machines multisensory perception under attacks. We use the audio-visual event recognition task against multimodal adversarial attacks as a proxy to investigate the robustness of audio-visual learning. We attack audio, visual, and both modalities to explore whether...	false	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	228,552
2401.15569	Efficient Tuning and Inference for Large Language Models on Textual Graphs	Rich textual and topological information of textual graphs need to be modeled in real-world applications such as webpages, e-commerce, and academic articles. Practitioners have been long following the path of adopting a shallow text encoder and a subsequent graph neural network (GNN) to solve this problem. In light of ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	424,502
1006.2565	State-Dependent Relay Channel with Private Messages with Partial Causal and Non-Causal Channel State Information	In this paper, we introduce a discrete memoryless State-Dependent Relay Channel with Private Messages (SD-RCPM) as a generalization of the state-dependent relay channel. We investigate two main cases: SD-RCPM with non-causal Channel State Information (CSI), and SD-RCPM with causal CSI. In each case, it is assumed that ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,774
1708.03065	Heterogeneous Networks with Power-Domain NOMA: Coverage, Throughput and Power Allocation Analysis	In a heterogeneous cellular network (HetNet), consider that a base station in the HetNet is able to simultaneously schedule and serve K users in the downlink by performing the power-domain non-orthogonal multiple access (NOMA) scheme. This paper aims at the preliminary study on the downlink coverage and throughput perf...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,711
1805.11728	Sapphire: Querying RDF Data Made Simple	RDF data in the linked open data (LOD) cloud is very valuable for many different applications. In order to unlock the full value of this data, users should be able to issue complex queries on the RDF datasets in the LOD cloud. SPARQL can express such complex queries, but constructing SPARQL queries can be a challenge t...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	98,991
2007.10534	Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features	In this digital age of news consumption, a news reader has the ability to react, express and share opinions with others in a highly interactive and fast manner. As a consequence, fake news has made its way into our daily life because of very limited capacity to verify news on the Internet by large companies as well as ...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	188,297
2202.00995	MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data	MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data. For the accurate prediction of MD-GAN, sufficient information on the dynamics of a part of the system should be included with the training data. Therefore, the selection...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,336
2101.12252	Gaussian Process Latent Class Choice Models	We present a Gaussian Process - Latent Class Choice Model (GP-LCCM) to integrate a non-parametric class of probabilistic machine learning within discrete choice models (DCMs). Gaussian Processes (GPs) are kernel-based algorithms that incorporate expert knowledge by assuming priors over latent functions rather than prio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	217,522
2104.10868	Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting	Crowd counting has drawn much attention due to its importance in safety-critical surveillance systems. Especially, deep neural network (DNN) methods have significantly reduced estimation errors for crowd counting missions. Recent studies have demonstrated that DNNs are vulnerable to adversarial attacks, i.e., normal im...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,745
1811.07350	Policy Optimization with Model-based Explorations	Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games. However, these methods suffer from high variances and high sample complexity. On the other hand, model-based reinforcement learning method...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,744
1008.3306	Modelling of Multi-Agent Systems: Experiences with Membrane Computing and Future Challenges	Formal modelling of Multi-Agent Systems (MAS) is a challenging task due to high complexity, interaction, parallelism and continuous change of roles and organisation between agents. In this paper we record our research experience on formal modelling of MAS. We review our research throughout the last decade, by describin...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	7,312
2010.07610	A Methodology for Ethics-by-Design AI Systems: Dealing with Human Value Conflicts	The introduction of artificial intelligence into activities traditionally carried out by human beings produces brutal changes. This is not without consequences for human values. This paper is about designing and implementing models of ethical behaviors in AI-based systems, and more specifically it presents a methodolog...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	200,887
2108.02664	A method to compute the communicability of nodes through causal paths in temporal networks	We present a method aimed to compute the communicability (broadcast and receive) of nodes through causal paths in temporal networks. The method considers all possible combinations of chronologically ordered products of adjacency matrices of the network snapshots and by means of a damping procedure favors the paths that...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	249,406
1909.10114	Gridless Angular Domain Channel Estimation for mmWave Massive MIMO System With One-Bit Quantization Via Approximate Message Passing	We develop a direction of arrival (DoA) and channel estimation algorithm for the one-bit quantized millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system. By formulating the estimation problem as a noisy one-bit compressed sensing problem, we propose a computationally efficient gridless solution ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	146,450
1506.05900	Representation Learning for Clustering: A Statistical Framework	We address the problem of communicating domain knowledge from a user to the designer of a clustering algorithm. We propose a protocol in which the user provides a clustering of a relatively small random sample of a data set. The algorithm designer then uses that sample to come up with a data representation under which ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	44,350
2206.03795	NOMA-based Improper Signaling for Multicell MISO RIS-assisted Broadcast Channels	In this paper, we study the performance of reconfigurable intelligent surfaces (RISs) in a multicell broadcast channel (BC) that employs improper Gaussian signaling (IGS) jointly with non-orthogonal multiple access (NOMA) to optimize either the minimum-weighted rate or the energy efficiency (EE) of the network. We show...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	301,409
2407.08861	A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models	In this article, we propose a novel standalone hybrid Spiking-Convolutional Neural Network (SC-NN) model and test on using image inpainting tasks. Our approach uses the unique capabilities of SNNs, such as event-based computation and temporal processing, along with the strong representation learning abilities of CNNs, ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	472,334
2106.14617	Optimized Wireless Control and Telemetry Network for Mobile Soccer Robots	In a diverse set of robotics applications, including RoboCup categories, mobile robots require control commands to interact with surrounding environment correctly. These control commands should come wirelessly to not interfere in robots' movement; also, the communication has a set of requirements, including low latency...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	243,465
1508.02959	Mountain Peak Detection in Online Social Media	We present a system for the classification of mountain panoramas from user-generated photographs followed by identification and extraction of mountain peaks from those panoramas. We have developed an automatic technique that, given as input a geo-tagged photograph, estimates its FOV (Field Of View) and the direction of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	45,955
2305.09758	A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot	Multimedia content, such as advertisements and story videos, exhibit a rich blend of creativity and multiple modalities. They incorporate elements like text, visuals, audio, and storytelling techniques, employing devices like emotions, symbolism, and slogans to convey meaning. There is a dearth of large annotated train...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	364,758
2306.04528	PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts	The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts. In response to this vital need, we introduce PromptRobust, a robustness benchmark designed to measure LLMs' resilience to adversarial prompts. This study uses a...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	371,784
2208.01537	Optimal Friendly Jamming and Transmit Power Allocation in RIS-assisted Secure Communication	This paper analyzes the secrecy performance of a reconfigurable intelligent surface (RIS) assisted wireless communication system with a friendly jammer in the presence of an eavesdropper. The friendly jammer enhances the secrecy by introducing artificial noise towards the eavesdropper without degrading the reception at...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	311,191
2206.08181	ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization	Graph Neural Networks (GNNs) have attracted much attention due to their ability in learning representations from graph-structured data. Despite the successful applications of GNNs in many domains, the optimization of GNNs is less well studied, and the performance on node classification heavily suffers from the long-tai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	303,032
2006.09239	Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts	Accurate estimation of aleatoric and epistemic uncertainty is crucial to build safe and reliable systems. Traditional approaches, such as dropout and ensemble methods, estimate uncertainty by sampling probability predictions from different submodels, which leads to slow uncertainty estimation at inference time. Recent ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,482
1707.08115	A novel CS Beamformer root-MUSIC algorithm and its subspace deviation analysis	Subspace based techniques for direction of arrival (DOA) estimation need large amount of snapshots to detect source directions accurately. This poses a problem in the form of computational burden on practical applications. The introduction of compressive sensing (CS) to solve this issue has become a norm in the last de...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	77,751
2405.20387	Sensitivity Analysis for Piecewise-Affine Approximations of Nonlinear Programs with Polytopic Constraints	Nonlinear Programs (NLPs) are prevalent in optimization-based control of nonlinear systems. Solving general NLPs is computationally expensive, necessitating the development of fast hardware or tractable suboptimal approximations. This paper investigates the sensitivity of the solutions of NLPs with polytopic constraint...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	459,319
2411.09820	WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking	While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potentia...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	508,377
1006.2977	Algebraic Constructions of Graph-Based Nested Codes from Protographs	Nested codes have been employed in a large number of communication applications as a specific case of superposition codes, for example to implement binning schemes in the presence of noise, in joint network-channel coding, or in physical-layer secrecy. Whereas nested lattice codes have been proposed recently for contin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,798
1811.08622	Angular Triplet-Center Loss for Multi-view 3D Shape Retrieval	How to obtain the desirable representation of a 3D shape, which is discriminative across categories and polymerized within classes, is a significant challenge in 3D shape retrieval. Most existing 3D shape retrieval methods focus on capturing strong discriminative shape representation with softmax loss for the classific...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,090
2308.15464	A Comparative Study of Loss Functions: Traffic Predictions in Regular and Congestion Scenarios	Spatiotemporal graph neural networks have achieved state-of-the-art performance in traffic forecasting. However, they often struggle to forecast congestion accurately due to the limitations of traditional loss functions. While accurate forecasting of regular traffic conditions is crucial, a reliable AI system must also...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	388,690
2207.06569	Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting	The practical success of overparameterized neural networks has motivated the recent scientific study of interpolating methods, which perfectly fit their training data. Certain interpolating methods, including neural networks, can fit noisy training data without catastrophically bad test performance, in defiance of stan...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	307,919
2411.02068	Model Integrity when Unlearning with T2I Diffusion Models	The rapid advancement of text-to-image Diffusion Models has led to their widespread public accessibility. However these models, trained on large internet datasets, can sometimes generate undesirable outputs. To mitigate this, approximate Machine Unlearning algorithms have been proposed to modify model weights to reduce...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	505,341
2303.01428	PuSHR: A Multirobot System for Nonprehensile Rearrangement	We focus on the problem of rearranging a set of objects with a team of car-like robot pushers built using off-the-shelf components. Maintaining control of pushed objects while avoiding collisions in a tight space demands highly coordinated motion that is challenging to execute on constrained hardware. Centralized repla...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	348,965
2310.19574	Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Snow Layers in Radar Echograms	Airborne radar sensors capture the profile of snow layers present on top of an ice sheet. Accurate tracking of these layers is essential to calculate their thicknesses, which are required to investigate the contribution of polar ice cap melt to sea-level rise. However, automatically processing the radar echograms to de...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	404,042
1311.6227	Experience of Developing a Meta-Semantic Search Engine	Thinking of todays web search scenario which is mainly keyword based, leads to the need of effective and meaningful search provided by Semantic Web. Existing search engines are vulnerable to provide relevant answers to users query due to their dependency on simple data available in web pages. On other hand, semantic se...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	28,634
2109.13441	DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs	Dynamic graph embedding has gained great attention recently due to its capability of learning low dimensional graph representations for complex temporal graphs with high accuracy. However, recent advances mostly focus on learning node embeddings as deterministic "vectors" for static graphs yet disregarding the key grap...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	257,636
1208.4316	An Online Character Recognition System to Convert Grantha Script to Malayalam	This paper presents a novel approach to recognize Grantha, an ancient script in South India and converting it to Malayalam, a prevalent language in South India using online character recognition mechanism. The motivation behind this work owes its credit to (i) developing a mechanism to recognize Grantha script in this ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	18,201
2407.17783	How Lightweight Can A Vision Transformer Be	In this paper, we explore a strategy that uses Mixture-of-Experts (MoE) to streamline, rather than augment, vision transformers. Each expert in an MoE layer is a SwiGLU feedforward network, where V and W2 are shared across the layer. No complex attention or convolutional mechanisms are employed. Depth-wise scaling is a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	476,113
2109.12085	Text-based NP Enrichment	Understanding the relations between entities denoted by NPs in a text is a critical part of human-like natural language understanding. However, only a fraction of such relations is covered by standard NLP tasks and benchmarks nowadays. In this work, we propose a novel task termed text-based NP enrichment (TNE), in whic...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	257,157
2104.08631	Training Humans to Train Robots Dynamic Motor Skills	Learning from demonstration (LfD) is commonly considered to be a natural and intuitive way to allow novice users to teach motor skills to robots. However, it is important to acknowledge that the effectiveness of LfD is heavily dependent on the quality of teaching, something that may not be assured with novices. It rema...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	230,882
2311.12821	Advancing The Rate-Distortion-Computation Frontier For Neural Image Compression	The rate-distortion performance of neural image compression models has exceeded the state-of-the-art for non-learned codecs, but neural codecs are still far from widespread deployment and adoption. The largest obstacle is having efficient models that are feasible on a wide variety of consumer hardware. Comparative rese...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	409,495
2104.07365	D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning	The convergence speed of machine learning models trained with Federated Learning is significantly affected by heterogeneous data partitions, even more so in a fully decentralized setting without a central server. In this paper, we show that the impact of label distribution skew, an important type of data heterogeneity,...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	230,389
1303.5431	Intuitions about Ordered Beliefs Leading to Probabilistic Models	The general use of subjective probabilities to model belief has been justified using many axiomatic schemes. For example, ?consistent betting behavior' arguments are well-known. To those not already convinced of the unique fitness and generality of probability models, such justifications are often unconvincing. The pre...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,119
2410.06481	Leaf Stripping on Uniform Attachment Trees	In this note we analyze the performance of a simple root-finding algorithm in uniform attachment trees. The leaf-stripping algorithm recursively removes all leaves of the tree for a carefully chosen number of rounds. We show that, with probability $1 - \epsilon$, the set of remaining vertices contains the root and has ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	496,221
2311.08835	Correlation-Guided Query-Dependency Calibration for Video Temporal Grounding	Temporal Grounding is to identify specific moments or highlights from a video corresponding to textual descriptions. Typical approaches in temporal grounding treat all video clips equally during the encoding process regardless of their semantic relevance with the text query. Therefore, we propose Correlation-Guided DEt...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	407,887
2310.13258	ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting	Seamless human-robot manipulation in close proximity relies on accurate forecasts of human motion. While there has been significant progress in learning forecast models at scale, when applied to manipulation tasks, these models accrue high errors at critical transition points leading to degradation in downstream planni...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	401,365
1306.5850	Practical Secrecy: Bridging the Gap between Cryptography and Physical Layer Security	Current security techniques can be implemented either by requiring a secret key exchange or depending on assumptions about the communication channels. In this paper, we show that, by using a physical layer technique known as artificial noise, it is feasible to protect secret data without any form of secret key exchange...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,437
2209.08924	HVC-Net: Unifying Homography, Visibility, and Confidence Learning for Planar Object Tracking	Robust and accurate planar tracking over a whole video sequence is vitally important for many vision applications. The key to planar object tracking is to find object correspondences, modeled by homography, between the reference image and the tracked image. Existing methods tend to obtain wrong correspondences with cha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,316
2405.06964	ManiFoundation Model for General-Purpose Robotic Manipulation of Contact Synthesis with Arbitrary Objects and Robots	To substantially enhance robot intelligence, there is a pressing need to develop a large model that enables general-purpose robots to proficiently undertake a broad spectrum of manipulation tasks, akin to the versatile task-planning ability exhibited by LLMs. The vast diversity in objects, robots, and manipulation task...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	453,520
1209.4316	Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics	We analyze representative ill-posed scenarios of tomographic PIV with a focus on conditions for unique volume reconstruction. Based on sparse random seedings of a region of interest with small particles, the corresponding systems of linear projection equations are probabilistically analyzed in order to determine (i) th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,637
1911.06479	On Model Robustness Against Adversarial Examples	We study the model robustness against adversarial examples, referred to as small perturbed input data that may however fool many state-of-the-art deep learning models. Unlike previous research, we establish a novel theory addressing the robustness issue from the perspective of stability of the loss function in the smal...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	153,552
2405.10933	Learning low-degree quantum objects	We consider the problem of learning low-degree quantum objects up to $\varepsilon$-error in $\ell_2$-distance. We show the following results: $(i)$ unknown $n$-qubit degree-$d$ (in the Pauli basis) quantum channels and unitaries can be learned using $O(1/\varepsilon^d)$ queries (independent of $n$), $(ii)$ polynomials ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	454,933
1811.01437	QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks	Adversarial examples have emerged as a significant threat to machine learning algorithms, especially to the convolutional neural networks (CNNs). In this paper, we propose two quantization-based defense mechanisms, Constant Quantization (CQ) and Trainable Quantization (TQ), to increase the robustness of CNNs against ad...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	112,364
2403.01112	Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning	In cooperative multi-agent reinforcement learning (MARL), agents aim to achieve a common goal, such as defeating enemies or scoring a goal. Existing MARL algorithms are effective but still require significant learning time and often get trapped in local optima by complex tasks, subsequently failing to discover a goal-r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	434,264
2304.10712	Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World	Infrared imaging systems have a vast array of potential applications in pedestrian detection and autonomous driving, and their safety performance is of great concern. However, few studies have explored the safety of infrared imaging systems in real-world settings. Previous research has used physical perturbations such ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	359,523
2010.11366	Random Coordinate Underdamped Langevin Monte Carlo	The Underdamped Langevin Monte Carlo (ULMC) is a popular Markov chain Monte Carlo sampling method. It requires the computation of the full gradient of the log-density at each iteration, an expensive operation if the dimension of the problem is high. We propose a sampling method called Random Coordinate ULMC (RC-ULMC), ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	202,222
2404.09943	Novel Joint Estimation and Decoding Metrics for Short-Block length Transmission Systems	This paper presents Bit-Interleaved Coded Modulation metrics for joint estimation detection using training or reference signal transmission strategies for short to long block length channels. We show that it is possible to enhance the performance and sensitivity through joint detection-estimation compared to standard r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	446,894
1609.04117	Network learning via multi-agent inverse transportation problems	Despite the ubiquity of transportation data, methods to infer the state parameters of a network either ignore sensitivity of route decisions, require route enumeration for parameterizing descriptive models of route selection, or require complex bilevel models of route assignment behavior. These limitations prevent mode...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	60,964
2004.11369	Investigating similarities and differences between South African and Sierra Leonean school outcomes using Machine Learning	Available or adequate information to inform decision making for resource allocation in support of school improvement is a critical issue globally. In this paper, we apply machine learning and education data mining techniques on education big data to identify determinants of high schools' performance in two African coun...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	173,889
2411.09279	A Comparative Analysis of Electricity Consumption Flexibility in Different Industrial Plant Configurations	The flexibility of industrial power consumption plays a key role in the transition to renewable energy systems, contributing to grid stability, cost reduction and decarbonization efforts. This paper presents a novel methodology to quantify and optimize the flexibility of electricity consumption in manufacturing plants....	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	508,197
2501.04733	AI-Driven Reinvention of Hydrological Modeling for Accurate Predictions and Interpretation to Transform Earth System Modeling	Traditional equation-driven hydrological models often struggle to accurately predict streamflow in challenging regional Earth systems like the Tibetan Plateau, while hybrid and existing algorithm-driven models face difficulties in interpreting hydrological behaviors. This work introduces HydroTrace, an algorithm-driven...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	523,324
1508.00703	Parameter Database : Data-centric Synchronization for Scalable Machine Learning	We propose a new data-centric synchronization framework for carrying out of machine learning (ML) tasks in a distributed environment. Our framework exploits the iterative nature of ML algorithms and relaxes the application agnostic bulk synchronization parallel (BSP) paradigm that has previously been used for distribut...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	45,706
2306.07201	LTCR: Long-Text Chinese Rumor Detection Dataset	False information can spread quickly on social media, negatively influencing the citizens' behaviors and responses to social events. To better detect all of the fake news, especially long texts which are harder to find completely, a Long-Text Chinese Rumor detection dataset named LTCR is proposed. The LTCR dataset prov...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	372,924
2404.01705	Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model	High-resolution remotely sensed images pose a challenge for commonly used semantic segmentation methods such as Convolutional Neural Network (CNN) and Vision Transformer (ViT). CNN-based methods struggle with handling such high-resolution images due to their limited receptive field, while ViT faces challenges in handli...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,541
1902.02308	Decentralized Flood Forecasting Using Deep Neural Networks	Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management. Conventional methods that aim to predict water levels in streams use advanced hydrological models still lack of giving accurate forecasts everywhere. This study aims to explore arti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,848
1110.0305	Significant communities in large sparse networks	Researchers use community-detection algorithms to reveal large-scale organization in biological and social networks, but community detection is useful only if the communities are significant and not a result of noisy data. To assess the statistical significance of the network communities, or the robustness of the detec...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,453
2007.01760	Explainable Deep One-Class Classification	Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this transformation is highly non-linear, finding interpretations poses a significant challenge. In this paper we present an explainable deep one-c...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	185,525
2206.03592	Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines	Online travel agencies (OTA's) advertise their website offers on meta-search bidding engines. The problem of predicting the number of clicks a hotel would receive for a given bid amount is an important step in the management of an OTA's advertisement campaign on a meta-search engine, because bid times number of clicks ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,335
2402.18719	MaxCUCL: Max-Consensus with Deterministic Convergence in Networks with Unreliable Communication	In this paper, we present a novel distributed algorithm (herein called MaxCUCL) designed to guarantee that max-consensus is reached in networks characterized by unreliable communication links (i.e., links suffering from packet drops). Our proposed algorithm is the first algorithm that achieves max-consensus in a determ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	433,531
2307.13510	HeightFormer: Explicit Height Modeling without Extra Data for Camera-only 3D Object Detection in Bird's Eye View	Vision-based Bird's Eye View (BEV) representation is an emerging perception formulation for autonomous driving. The core challenge is to construct BEV space with multi-camera features, which is a one-to-many ill-posed problem. Diving into all previous BEV representation generation methods, we found that most of them fa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	381,610
2308.07187	On the Asymptotic Nonnegative Rank of Matrices and its Applications in Information Theory	In this paper, we study the asymptotic nonnegative rank of matrices, which characterizes the asymptotic growth of the nonnegative rank of fixed nonnegative matrices under the Kronecker product. This quantity is important since it governs several notions in information theory such as the so-called exact R\'enyi common i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	385,417
2305.07465	Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systems	Generative Artificial Intelligence systems have been developed for image, code, story, and game generation with the goal of facilitating human creativity. Recent work on neural generative systems has emphasized one particular means of interacting with AI systems: the user provides a specification, usually in the form o...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	363,906
2209.08618	Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data	In many scenarios, it is necessary to monitor a complex system via a time-series of observations and determine when anomalous exogenous events have occurred so that relevant actions can be taken. Determining whether current observations are abnormal is challenging. It requires learning an extrapolative probabilistic mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	318,193
1810.10789	Perceptual Visual Interactive Learning	Supervised learning methods are widely used in machine learning. However, the lack of labels in existing data limits the application of these technologies. Visual interactive learning (VIL) compared with computers can avoid semantic gap, and solve the labeling problem of small label quantity (SLQ) samples in a groundbr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,364
2206.01176	From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics*	Graphs have often been used to answer questions about the interaction between real-world entities by taking advantage of their capacity to represent complex topologies. Complex networks are known to be graphs that capture such non-trivial topologies; they are able to represent human phenomena such as epidemic processes...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	300,374
2210.08083	Reference Based Color Transfer for Medical Volume Rendering	The benefits of medical imaging are enormous. Medical images provide considerable amounts of anatomical information and this facilitates medical practitioners in performing effective disease diagnosis and deciding upon the best course of medical treatment. A transition from traditional monochromatic medical images like...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	323,973
2410.22730	Extensional Properties of Recurrent Neural Networks	A property of a recurrent neural network (RNN) is called \emph{extensional} if, loosely speaking, it is a property of the function computed by the RNN rather than a property of the RNN algorithm. Many properties of interest in RNNs are extensional, for example, robustness against small changes of input or good clusteri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	503,747
1810.05357	On The Equivalence of Tries and Dendrograms - Efficient Hierarchical Clustering of Traffic Data	The widespread use of GPS-enabled devices generates voluminous and continuous amounts of traffic data but analyzing such data for interpretable and actionable insights poses challenges. A hierarchical clustering of the trips has many uses such as discovering shortest paths, common routes and often traversed areas. Howe...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	110,210
2112.03298	Automation Of Transiting Exoplanet Detection, Identification and Habitability Assessment Using Machine Learning Approaches	We are at a unique timeline in the history of human evolution where we may be able to discover earth-like planets around stars outside our solar system where conditions can support life or even find evidence of life on those planets. With the launch of several satellites in recent years by NASA, ESA, and other major sp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	270,153
1601.05403	Semantic Word Clusters Using Signed Normalized Graph Cuts	Vector space representations of words capture many aspects of word similarity, but such methods tend to make vector spaces in which antonyms (as well as synonyms) are close to each other. We present a new signed spectral normalized graph cut algorithm, signed clustering, that overlays existing thesauri upon distributio...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	51,119
2203.16256	Research topic trend prediction of scientific papers based on spatial enhancement and dynamic graph convolution network	In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Accurately and effectively predicting the trends of future research topics can help researchers discover future research hotspots. However, due to the increasingl...	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	false	288,715
1711.03525	Improving the redundancy of Knuth's balancing scheme for packet transmission systems	A simple scheme was proposed by Knuth to generate binary balanced codewords from any information word. However, this method is limited in the sense that its redundancy is twice that of the full sets of balanced codes. The gap between Knuth's algorithm's redundancy and that of the full sets of balanced codes is signific...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	84,225
1409.4481	Real-time Crowd Tracking using Parameter Optimized Mixture of Motion Models	We present a novel, real-time algorithm to track the trajectory of each pedestrian in moderately dense crowded scenes. Our formulation is based on an adaptive particle-filtering scheme that uses a combination of various multi-agent heterogeneous pedestrian simulation models. We automatically compute the optimal paramet...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	36,079
2107.01858	Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities	We present a framework for automating generative deep learning with a specific focus on artistic applications. The framework provides opportunities to hand over creative responsibilities to a generative system as targets for automation. For the definition of targets, we adopt core concepts from automated machine learni...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,620

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