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
2310.12403	Cooperative Minibatching in Graph Neural Networks	Training large scale Graph Neural Networks (GNNs) requires significant computational resources, and the process is highly data-intensive. One of the most effective ways to reduce resource requirements is minibatch training coupled with graph sampling. GNNs have the unique property that items in a minibatch have overlap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	400,997
2203.13470	Interactive Style Transfer: All is Your Palette	Neural style transfer (NST) can create impressive artworks by transferring reference style to content image. Current image-to-image NST methods are short of fine-grained controls, which are often demanded by artistic editing. To mitigate this limitation, we propose a drawing-like interactive style transfer (IST) method...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,642
2401.06272	Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors	Purpose: Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia. Clinicians routinely measure nodal size to monitor disease progression, confirm metastatic cancer, and assess treatment response. However, variations in their shapes and appearances make it c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	421,094
2207.13266	Sparse Deep Neural Network for Nonlinear Partial Differential Equations	More competent learning models are demanded for data processing due to increasingly greater amounts of data available in applications. Data that we encounter often have certain embedded sparsity structures. That is, if they are represented in an appropriate basis, their energies can concentrate on a small number of bas...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	310,246
2205.02697	Mode Reduction for Markov Jump Systems	Switched systems are capable of modeling processes with underlying dynamics that may change abruptly over time. To achieve accurate modeling in practice, one may need a large number of modes, but this may in turn increase the model complexity drastically. Existing work on reducing system complexity mainly considers sta...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	295,027
2211.15262	HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeria	Social media platforms allow users to freely share their opinions about issues or anything they feel like. However, they also make it easier to spread hate and abusive content. The Fulani ethnic group has been the victim of this unfortunate phenomenon. This paper introduces the HERDPhobia - the first annotated hate spe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	333,181
2102.04487	Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning	Communication of model updates between client nodes and the central aggregating server is a major bottleneck in federated learning, especially in bandwidth-limited settings and high-dimensional models. Gradient quantization is an effective way of reducing the number of bits required to communicate each model update, al...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	219,126
1801.06819	A Next-Best-Smell Approach for Remote Gas Detection with a Mobile Robot	The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. Using mobile robots for gas detection has several advantages and can reduce danger for humans. In our work, we address the problem of planning a path for a mobile robotic plat...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	88,681
2105.11397	Autonomous Kinetic Modeling of Biomass Pyrolysis using Chemical Reaction Neural Networks	Modeling the burning processes of biomass such as wood, grass, and crops is crucial for the modeling and prediction of wildland and urban fire behavior. Despite its importance, the burning of solid fuels remains poorly understood, which can be partly attributed to the unknown chemical kinetics of most solid fuels. Most...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	236,688
2501.02211	Examining the Robustness of Homogeneity Bias to Hyperparameter Adjustments in GPT-4	Vision-Language Models trained on massive collections of human-generated data often reproduce and amplify societal stereotypes. One critical form of stereotyping reproduced by these models is homogeneity bias-the tendency to represent certain groups as more homogeneous than others. We investigate how this bias responds...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	522,399
2304.05828	Measuring a Soft Resistive Strain Sensor Array by Solving the Resistor Network Inverse Problem	Soft robotics is applicable to a variety of domains due to the adaptability offered by the soft and compliant materials. To develop future intelligent soft robots, soft sensors that can capture deformation with nearly infinite degree-of-freedom are necessary. Soft sensor networks can address this problem, however, meas...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	357,757
2211.15990	Optimal Beam Training for mmWave Massive MIMO using 802.11ay	Beam training of 802.11 ad is a technology that helps accelerate the analog weighting vector (AWV) selection process under the constraint of the existing code-book for AWV. However, 5G milli-meter wave (mmWave) multiple-input-multiple-output (MIMO) system brings challenges to this new technology due to the higher order...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	333,476
2005.09109	Dynamic Knowledge embedding and tracing	The goal of knowledge tracing is to track the state of a student's knowledge as it evolves over time. This plays a fundamental role in understanding the learning process and is a key task in the development of an intelligent tutoring system. In this paper we propose a novel approach to knowledge tracing that combines t...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	177,812
2406.00012	FINED: Feed Instance-Wise Information Need with Essential and Disentangled Parametric Knowledge from the Past	Recommender models play a vital role in various industrial scenarios, while often faced with the catastrophic forgetting problem caused by the fast shifting data distribution. To alleviate this problem, a common approach is to reuse knowledge from the historical data. However, preserving the vast and fast-accumulating ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	459,650
2403.09635	Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models	In spite of their huge success, transformer models remain difficult to scale in depth. In this work, we develop a unified signal propagation theory and provide formulae that govern the moments of the forward and backward signal through the transformer model. Our framework can be used to understand and mitigate vanishin...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	437,851
2112.08418	Neural Network-based Power Flow Model	Power flow analysis is used to evaluate the flow of electricity in the power system network. Power flow calculation is used to determine the steady-state variables of the system, such as the voltage magnitude/phase angle of each bus and the active/reactive power flow on each branch. The DC power flow model is a popular...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	271,784
1303.5910	Ant Colony Optimization with a New Random Walk Model for Community Detection in Complex Networks	Detecting communities from complex networks has recently triggered great interest. Aiming at this problem, a new ant colony optimization strategy building on the Markov random walks theory, which is named as MACO, is proposed in this paper. The framework of ant colony optimization is taken as the basic framework in thi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	23,222
2310.11453	BitNet: Scaling 1-bit Transformers for Large Language Models	The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. In this work, we introduce BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. Specifically, we introduce BitLinear...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	400,647
1204.3343	Broadcast Search in Innovation Contests: Case for Hybrid Models	Organizations use broadcast search to identify new avenues of innovation. Research on innovation contests provides insights on why excellent ideas are created in a broadcast search. However, there is little research on how excellent ideas are selected. Drawing from the brainstorming literature we find that the selectio...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	15,480
2304.12602	Is deep learning a useful tool for the pure mathematician?	A personal and informal account of what a pure mathematician might expect when using tools from deep learning in their research.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	360,283
2412.08328	Th\'evenin Equivalent Parameters Identification Based on Statistical Characteristics of System Ambient Data	This paper proposes a novel method for identifying Th\'evenin equivalent parameters (TEP) in power system, based on the statistical characteristics of the system's stochastic response. The method leverages stochastic fluctuation data under steady-state grid conditions and applies sliding window techniques to compute se...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	516,047
2202.11200	Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications	Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency. To solve this problem, quantum deep learning (QDL) and distributed deep learning (DDL) ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	281,798
2308.07001	The Scientometrics and Reciprocality Underlying Co-Authorship Panels in Google Scholar Profiles	Online academic profiles are used by scholars to reflect a desired image to their online audience. In Google Scholar, scholars can select a subset of co-authors for presentation in a central location on their profile using a social feature called the Co-authroship panel. In this work, we examine whether scientometrics ...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	true	385,358
2502.01906	Rethinking Homogeneity of Vision and Text Tokens in Large Vision-and-Language Models	Large vision-and-language models (LVLMs) typically treat visual and textual embeddings as homogeneous inputs to a large language model (LLM). However, these inputs are inherently different: visual inputs are multi-dimensional and contextually rich, often pre-encoded by models like CLIP, while textual inputs lack this s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	530,083
1601.06068	Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing	One of the limitations of semantic parsing approaches to open-domain question answering is the lexicosyntactic gap between natural language questions and knowledge base entries -- there are many ways to ask a question, all with the same answer. In this paper we propose to bridge this gap by generating paraphrases of th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	51,213
2201.01283	Self-supervised Learning from 100 Million Medical Images	Building accurate and robust artificial intelligence systems for medical image assessment requires not only the research and design of advanced deep learning models but also the creation of large and curated sets of annotated training examples. Constructing such datasets, however, is often very costly -- due to the com...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	274,203
1903.10176	DeepRED: Deep Image Prior Powered by RED	Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. One such contribution, which is the focus of this paper, is the Deep Image Prio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,222
2402.04821	E(3)-Equivariant Mesh Neural Networks	Triangular meshes are widely used to represent three-dimensional objects. As a result, many recent works have address the need for geometric deep learning on 3D mesh. However, we observe that the complexities in many of these architectures does not translate to practical performance, and simple deep models for geometri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,599
1204.5345	Time-dependent wave selection for information processing in excitable media	We demonstrate an improved technique for implementing logic circuits in light-sensitive chemical excitable media. The technique makes use of the constant-speed propagation of waves along defined channels in an excitable medium based on the Belousov-Zhabotinsky reaction, along with the mutual annihilation of colliding w...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	15,643
2210.16273	SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathology	Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as a black box, exclude biomedical experts, and need extensive data. We introduce the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI), that integrates hypothesis-driven priors in a data-driven DL approach for r...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	327,284
1710.07435	Multipartite Pooling for Deep Convolutional Neural Networks	We propose a novel pooling strategy that learns how to adaptively rank deep convolutional features for selecting more informative representations. To this end, we exploit discriminative analysis to project the features onto a space spanned by the number of classes in the dataset under study. This maps the notion of lab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	82,936
2501.07602	An Explainable Pipeline for Machine Learning with Functional Data	Machine learning (ML) models have shown success in applications with an objective of prediction, but the algorithmic complexity of some models makes them difficult to interpret. Methods have been proposed to provide insight into these "black-box" models, but there is little research that focuses on supervised ML when t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	524,455
1611.05964	Reweighted Low-Rank Tensor Completion and its Applications in Video Recovery	This paper focus on recovering multi-dimensional data called tensor from randomly corrupted incomplete observation. Inspired by reweighted $l_1$ norm minimization for sparsity enhancement, this paper proposes a reweighted singular value enhancement scheme to improve tensor low tubular rank in the tensor completion proc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,101
1208.5269	Support Recovery with Sparsely Sampled Free Random Matrices	Consider a Bernoulli-Gaussian complex $n$-vector whose components are $V_i = X_i B_i$, with $X_i \sim \Cc\Nc(0,\Pc_x)$ and binary $B_i$ mutually independent and iid across $i$. This random $q$-sparse vector is multiplied by a square random matrix $\Um$, and a randomly chosen subset, of average size $n p$, $p \in [0,1]$...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,258
2412.10567	Cardiovascular Disease Detection By Leveraging Semi-Supervised Learning	Cardiovascular disease (CVD) persists as a primary cause of death on a global scale, which requires more effective and timely detection methods. Traditional supervised learning approaches for CVD detection rely heavily on large-labeled datasets, which are often difficult to obtain. This paper employs semi-supervised le...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	516,996
2411.11105	Label Sharing Incremental Learning Framework for Independent Multi-Label Segmentation Tasks	In a setting where segmentation models have to be built for multiple datasets, each with its own corresponding label set, a straightforward way is to learn one model for every dataset and its labels. Alternatively, multi-task architectures with shared encoders and multiple segmentation heads or shared weights with comp...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	508,922
2405.02781	Instantaneous Perception of Moving Objects in 3D	The perception of 3D motion of surrounding traffic participants is crucial for driving safety. While existing works primarily focus on general large motions, we contend that the instantaneous detection and quantification of subtle motions is equally important as they indicate the nuances in driving behavior that may be...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	451,912
2202.10145	Information Revelation Through Signalling	This paper studies a Stackelberg game wherein a sender (leader) attempts to shape the information of a less informed receiver (follower) who in turn takes an action that determines the payoff for both players. The sender chooses signals to maximize its own utility function while the receiver aims to ascertain the value...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	281,434
2010.09077	A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with Application to COVID-19	As the COVID-19 pandemic evolves, reliable prediction plays an important role for policy making. The classical infectious disease model SEIR (susceptible-exposed-infectious-recovered) is a compact yet simplistic temporal model. The data-driven machine learning models such as RNN (recurrent neural networks) can suffer i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	201,396
2305.05907	Evidence of Inter-state Coordination amongst State-backed Information Operations	Since 2018, Twitter has steadily released into the public domain content discovered on the platform and believed to be associated with information operations originating from more than a dozen state-backed organizations. Leveraging this dataset, we explore inter-state coordination amongst state-backed information opera...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	363,335
2309.11144	GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation	Cardiac structure segmentation from echocardiogram videos plays a crucial role in diagnosing heart disease. The combination of multi-view echocardiogram data is essential to enhance the accuracy and robustness of automated methods. However, due to the visual disparity of the data, deriving cross-view context informatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	393,303
2406.09179	Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning	The compelling goal of eradicating undesirable data behaviors, while preserving usual model functioning, underscores the significance of machine unlearning within the domain of large language models (LLMs). Recent research has begun to approach LLM unlearning via gradient ascent (GA) -- increasing the prediction risk f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	463,806
2406.01352	Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience	Inner Interpretability is a promising emerging field tasked with uncovering the inner mechanisms of AI systems, though how to develop these mechanistic theories is still much debated. Moreover, recent critiques raise issues that question its usefulness to advance the broader goals of AI. However, it has been overlooked...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	460,277
2109.04524	Fine Manipulation and Dynamic Interaction in Haptic Teleoperation	The teleoperation of robots enables remote intervention in distant and dangerous tasks without putting the operator in harm's way. However, remote operation faces fundamental challenges due to limits in communication delays. The proposed work improves the performances of teleoperation architecture based on Fractal Impe...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	254,428
2308.11015	Spectral Graphormer: Spectral Graph-based Transformer for Egocentric Two-Hand Reconstruction using Multi-View Color Images	We propose a novel transformer-based framework that reconstructs two high fidelity hands from multi-view RGB images. Unlike existing hand pose estimation methods, where one typically trains a deep network to regress hand model parameters from single RGB image, we consider a more challenging problem setting where we dir...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,967
2102.07878	A Hidden Challenge of Link Prediction: Which Pairs to Check?	The traditional setup of link prediction in networks assumes that a test set of node pairs, which is usually balanced, is available over which to predict the presence of links. However, in practice, there is no test set: the ground-truth is not known, so the number of possible pairs to predict over is quadratic in the ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	220,259
cs/0009018	A Resolution Calculus for Dynamic Semantics	This paper applies resolution theorem proving to natural language semantics. The aim is to circumvent the computational complexity triggered by natural language ambiguities like pronoun binding, by interleaving pronoun binding with resolution deduction. Therefore disambiguation is only applied to expression that actual...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	537,216
2210.12997	Are Current Decoding Strategies Capable of Facing the Challenges of Visual Dialogue?	Decoding strategies play a crucial role in natural language generation systems. They are usually designed and evaluated in open-ended text-only tasks, and it is not clear how different strategies handle the numerous challenges that goal-oriented multimodal systems face (such as grounding and informativeness). To answer...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	326,003
2107.08402	RobustFed: A Truth Inference Approach for Robust Federated Learning	Federated learning is a prominent framework that enables clients (e.g., mobile devices or organizations) to train a collaboratively global model under a central server's orchestration while keeping local training datasets' privacy. However, the aggregation step in federated learning is vulnerable to adversarial attacks...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	246,722
2402.01090	Scalable Higher-Order Tensor Product Spline Models	In the current era of vast data and transparent machine learning, it is essential for techniques to operate at a large scale while providing a clear mathematical comprehension of the internal workings of the method. Although there already exist interpretable semi-parametric regression methods for large-scale applicatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	425,850
2310.12046	Applications of ML-Based Surrogates in Bayesian Approaches to Inverse Problems	Neural networks have become a powerful tool as surrogate models to provide numerical solutions for scientific problems with increased computational efficiency. This efficiency can be advantageous for numerically challenging problems where time to solution is important or when evaluation of many similar analysis scenari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	400,882
cs/0502016	Stability Analysis for Regularized Least Squares Regression	We discuss stability for a class of learning algorithms with respect to noisy labels. The algorithms we consider are for regression, and they involve the minimization of regularized risk functionals, such as L(f) := 1/N sum_i (f(x_i)-y_i)^2+ lambda \|\|f\|\|_H^2. We shall call the algorithm `stable' if, when y_i is a noisy...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	538,542
2308.03306	Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing	Implicit Graph Neural Networks (GNNs) have achieved significant success in addressing graph learning problems recently. However, poorly designed implicit GNN layers may have limited adaptability to learn graph metrics, experience over-smoothing issues, or exhibit suboptimal convergence and generalization properties, po...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	383,989
2312.07857	Selecting the Number of Sensor Scans in Surveillance Operations	Searching for concealed threats within a surveillance region is an important role for military sensors. One prominent case is the search for a submarine by a helicopter deploying a dipping sonar in anti-submarine warfare. Another is the utilisation of an uncrewed aerial vehicle for remote detection of land mines. These...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	415,088
1707.02237	The 2017 Hands in the Million Challenge on 3D Hand Pose Estimation	We present the 2017 Hands in the Million Challenge, a public competition designed for the evaluation of the task of 3D hand pose estimation. The goal of this challenge is to assess how far is the state of the art in terms of solving the problem of 3D hand pose estimation as well as detect major failure and strength mod...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	76,665
2409.06522	Deep Learning for Koopman Operator Estimation in Idealized Atmospheric Dynamics	Deep learning is revolutionizing weather forecasting, with new data-driven models achieving accuracy on par with operational physical models for medium-term predictions. However, these models often lack interpretability, making their underlying dynamics difficult to understand and explain. This paper proposes methodolo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	487,156
2311.17008	An Investigation of Time Reversal Symmetry in Reinforcement Learning	One of the fundamental challenges associated with reinforcement learning (RL) is that collecting sufficient data can be both time-consuming and expensive. In this paper, we formalize a concept of time reversal symmetry in a Markov decision process (MDP), which builds upon the established structure of dynamically revers...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	411,119
1109.1865	Progressive versus Random Projections for Compressive Capture of Images, Lightfields and Higher Dimensional Visual Signals	Computational photography involves sophisticated capture methods. A new trend is to capture projection of higher dimensional visual signals such as videos, multi-spectral data and lightfields on lower dimensional sensors. Carefully designed capture methods exploit the sparsity of the underlying signal in a transformed ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	12,059
2109.12370	Interpretable Business Survival Prediction	The survival of a business is undeniably pertinent to its success. A key factor contributing to its continuity depends on its customers. The surge of location-based social networks such as Yelp, Dianping, and Foursquare has paved the way for leveraging user-generated content on these platforms to predict business survi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	257,259
2102.00367	Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features	Fine-grained visual classification is a challenging task that recognizes the sub-classes belonging to the same meta-class. Large inter-class similarity and intra-class variance is the main challenge of this task. Most exiting methods try to solve this problem by designing complex model structures to explore more minute...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	217,753
2409.13265	Towards LifeSpan Cognitive Systems	Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central to this is enabling continuous, high-frequency interactions, where the interactions are termed experiences. We refer to this envisioned syst...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	489,920
2104.03165	TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases from Chest X-ray Images	Tuberculosis (TB) remains a global health problem, and is the leading cause of death from an infectious disease. A crucial step in the treatment of tuberculosis is screening high risk populations and the early detection of the disease, with chest x-ray (CXR) imaging being the most widely-used imaging modality. As such,...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	228,996
1809.09672	BanditSum: Extractive Summarization as a Contextual Bandit	In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call our approach BanditSum as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to sum...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	108,751
2306.12845	A new 3-DOF 2T1R parallel mechanism: Topology design and kinematics	This article presents a new three-degree-of-freedom (3-DOF) parallel mechanism (PM) with two translations and one rotation (2T1R), designed based on the topological design theory of the parallel mechanism using position and orientation characteristics (POC). The PM is primarily intended for use in package sorting and d...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	375,082
1711.11556	ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes	Exploiting synthetic data to learn deep models has attracted increasing attention in recent years. However, the intrinsic domain difference between synthetic and real images usually causes a significant performance drop when applying the learned model to real world scenarios. This is mainly due to two reasons: 1) the m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,801
2005.05788	Noisy Density Evolution With Asymmetric Deviation Models	This paper considers low-density parity-check (LDPC) decoders affected by deviations introduced by the electronic device on which the decoder is implemented. Noisy density evolution (DE) that allows to theoretically study the performance of these LDPC decoders can only consider symmetric deviation models due to the all...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	176,830
2401.01141	Spiker+: a framework for the generation of efficient Spiking Neural Networks FPGA accelerators for inference at the edge	Including Artificial Neural Networks in embedded systems at the edge allows applications to exploit Artificial Intelligence capabilities directly within devices operating at the network periphery. This paper introduces Spiker+, a comprehensive framework for generating efficient, low-power, and low-area customized Spiki...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	419,244
2209.14142	From Zero to Production: Baltic-Ukrainian Machine Translation Systems to Aid Refugees	In this paper, we examine the development and usage of six low-resource machine translation systems translating between the Ukrainian language and each of the official languages of the Baltic states. We developed these systems in reaction to the escalating Ukrainian refugee crisis caused by the Russian military aggress...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	320,154
cs/0512045	Branch-and-Prune Search Strategies for Numerical Constraint Solving	When solving numerical constraints such as nonlinear equations and inequalities, solvers often exploit pruning techniques, which remove redundant value combinations from the domains of variables, at pruning steps. To find the complete solution set, most of these solvers alternate the pruning steps with branching steps,...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	539,140
2502.02079	Online Clustering of Dueling Bandits	The contextual multi-armed bandit (MAB) is a widely used framework for problems requiring sequential decision-making under uncertainty, such as recommendation systems. In applications involving a large number of users, the performance of contextual MAB can be significantly improved by facilitating collaboration among m...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	530,174
2005.00876	Conditional R\'enyi entropy and the relationships between R\'enyi capacities	The analogues of Arimoto's definition of conditional R\'enyi entropy and R\'enyi mutual information are explored for abstract alphabets. These quantities, although dependent on the reference measure, have some useful properties similar to those known in the discrete setting. In addition to laying out some such basic pr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	175,415
2407.12833	ESQA: Event Sequences Question Answering	Event sequences (ESs) arise in many practical domains including finance, retail, social networks, and healthcare. In the context of machine learning, event sequences can be seen as a special type of tabular data with annotated timestamps. Despite the importance of ESs modeling and analysis, little effort was made in ad...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	474,109
1607.00562	Integrated Task and Motion Planning for Multiple Robots under Path and Communication Uncertainties	We consider a problem called task ordering with path uncertainty (TOP-U) where multiple robots are provided with a set of task locations to visit in a bounded environment, but the length of the path between a pair of task locations is initially known only coarsely by the robots. The objective of the robots is to find t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	58,100
2010.09119	FADER: Fast Adversarial Example Rejection	Deep neural networks are vulnerable to adversarial examples, i.e., carefully-crafted inputs that mislead classification at test time. Recent defenses have been shown to improve adversarial robustness by detecting anomalous deviations from legitimate training samples at different layer representations - a behavior norma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	201,415
2202.01145	Relative Position Prediction as Pre-training for Text Encoders	Meaning is defined by the company it keeps. However, company is two-fold: It's based on the identity of tokens and also on their position (topology). We argue that a position-centric perspective is more general and useful. The classic MLM and CLM objectives in NLP are easily phrased as position predictions over the who...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	278,386
2102.03795	Unsupervised Sentence-embeddings by Manifold Approximation and Projection	The concept of unsupervised universal sentence encoders has gained traction recently, wherein pre-trained models generate effective task-agnostic fixed-dimensional representations for phrases, sentences and paragraphs. Such methods are of varying complexity, from simple weighted-averages of word vectors to complex lang...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	218,876
2008.03212	Managing caching strategies for stream reasoning with reinforcement learning	Efficient decision-making over continuously changing data is essential for many application domains such as cyber-physical systems, industry digitalization, etc. Modern stream reasoning frameworks allow one to model and solve various real-world problems using incremental and continuous evaluation of programs as new dat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	190,837
1401.6790	Optimal Power Allocation in Block Fading Gaussian Channels with Causal CSI and Secrecy Constraints	The optimal power allocation that maximizes the secrecy capacity of block fading Gaussian (BF-Gaussian) networks with causal channel state information (CSI), M-block delay tolerance and a frame based power constraint is examined. In particular, we formulate the secrecy capacity maximization as a dynamic program. We pro...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	30,402
2310.16574	Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regression	We present a mapping algorithm to compute large-scale magnetic field maps in indoor environments with approximate Gaussian process (GP) regression. Mapping the spatial variations in the ambient magnetic field can be used for localization algorithms in indoor areas. To compute such a map, GP regression is a suitable too...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	402,783
2108.09684	Rainfall-runoff prediction using a Gustafson-Kessel clustering based Takagi-Sugeno Fuzzy model	A rainfall-runoff model predicts surface runoff either using a physically-based approach or using a systems-based approach. Takagi-Sugeno (TS) Fuzzy models are systems-based approaches and a popular modeling choice for hydrologists in recent decades due to several advantages and improved accuracy in prediction over oth...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	251,688
2306.02719	Multiple output samples per input in a single-output Gaussian process	The standard Gaussian Process (GP) only considers a single output sample per input in the training set. Datasets for subjective tasks, such as spoken language assessment, may be annotated with output labels from multiple human raters per input. This paper proposes to generalise the GP to allow for these multiple output...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	371,026
2203.11784	Controlling the average degree in random power-law networks	We describe a procedure that allows continuously tuning the average degree $\langle k \rangle$ of uncorrelated networks with power-law degree distribution $p(k)$. Inn order to do this, we modify the low-$k$ region of $p(k)$, while preserving the large-$k$ tail up to a cutoff. Then, we use the modified $p(k)$ to obtain ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	287,038
2310.11663	High Efficiency Polymer based Direct Multi-jet Impingement Cooling Solution for High Power Devices	Liquid jet impingement cooling is an efficient cooling technique where the liquid coolant is directly ejected from nozzles on the chip backside resulting in a high cooling efficiency due to the absence of the TIM and the lateral temperature gradient. In literature, several Si-fabrication based impingement coolers with ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	400,726
2010.06545	Toward Few-step Adversarial Training from a Frequency Perspective	We investigate adversarial-sample generation methods from a frequency domain perspective and extend standard $l_{\infty}$ Projected Gradient Descent (PGD) to the frequency domain. The resulting method, which we call Spectral Projected Gradient Descent (SPGD), has better success rate compared to PGD during early steps o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	200,524
1606.05925	Graph based manifold regularized deep neural networks for automatic speech recognition	Deep neural networks (DNNs) have been successfully applied to a wide variety of acoustic modeling tasks in recent years. These include the applications of DNNs either in a discriminative feature extraction or in a hybrid acoustic modeling scenario. Despite the rapid progress in this area, a number of challenges remain ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	57,495
1910.13092	Bayesian Optimization with Unknown Search Space	Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy for the Bayesian optimization. We devise a strategy to guarantee that in iterative expansions of the search space, our method can find a point whose f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,296
1901.10673	Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric Learning	This paper presents an approach for learning invariant features for object affordance understanding. One of the major problems for a robotic agent acquiring a deeper understanding of affordances is finding sensory-grounded semantics. Being able to understand what in the representation of an object makes the object affo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	120,080
2410.09111	IceDiff: High Resolution and High-Quality Sea Ice Forecasting with Generative Diffusion Prior	Variation of Arctic sea ice has significant impacts on polar ecosystems, transporting routes, coastal communities, and global climate. Tracing the change of sea ice at a finer scale is paramount for both operational applications and scientific studies. Recent pan-Arctic sea ice forecasting methods that leverage advance...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	497,440
2312.12633	Long-run Behaviour of Multi-fidelity Bayesian Optimisation	Multi-fidelity Bayesian Optimisation (MFBO) has been shown to generally converge faster than single-fidelity Bayesian Optimisation (SFBO) (Poloczek et al. (2017)). Inspired by recent benchmark papers, we are investigating the long-run behaviour of MFBO, based on observations in the literature that it might under-perfor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	417,029
2303.10993	A Survey on Oversmoothing in Graph Neural Networks	Node features of graph neural networks (GNNs) tend to become more similar with the increase of the network depth. This effect is known as over-smoothing, which we axiomatically define as the exponential convergence of suitable similarity measures on the node features. Our definition unifies previous approaches and give...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	352,665
2401.15479	Navigating the Post-API Dilemma \| Search Engine Results Pages Present a Biased View of Social Media Data	Recent decisions to discontinue access to social media APIs are having detrimental effects on Internet research and the field of computational social science as a whole. This lack of access to data has been dubbed the Post-API era of Internet research. Fortunately, popular search engines have the means to crawl, captur...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	424,462
2104.08135	Sharp bounds for the number of regions of maxout networks and vertices of Minkowski sums	We present results on the number of linear regions of the functions that can be represented by artificial feedforward neural networks with maxout units. A rank-k maxout unit is a function computing the maximum of $k$ linear functions. For networks with a single layer of maxout units, the linear regions correspond to th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	230,674
2301.09742	Topological Understanding of Neural Networks, a survey	We look at the internal structure of neural networks which is usually treated as a black box. The easiest and the most comprehensible thing to do is to look at a binary classification and try to understand the approach a neural network takes. We review the significance of different activation functions, types of networ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	341,589
2109.07465	On the Limits of Minimal Pairs in Contrastive Evaluation	Minimal sentence pairs are frequently used to analyze the behavior of language models. It is often assumed that model behavior on contrastive pairs is predictive of model behavior at large. We argue that two conditions are necessary for this assumption to hold: First, a tested hypothesis should be well-motivated, since...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	255,536
2310.18377	Large-scale Foundation Models and Generative AI for BigData Neuroscience	Recent advances in machine learning have made revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised lea...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	403,519
2105.04014	DiagSet: a dataset for prostate cancer histopathological image classification	Cancer diseases constitute one of the most significant societal challenges. In this paper, we introduce a novel histopathological dataset for prostate cancer detection. The proposed dataset, consisting of over 2.6 million tissue patches extracted from 430 fully annotated scans, 4675 scans with assigned binary diagnoses...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	234,346
2401.16212	Better Call GPT, Comparing Large Language Models Against Lawyers	This paper presents a groundbreaking comparison between Large Language Models and traditional legal contract reviewers, Junior Lawyers and Legal Process Outsourcers. We dissect whether LLMs can outperform humans in accuracy, speed, and cost efficiency during contract review. Our empirical analysis benchmarks LLMs again...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	424,738
1901.10051	Knowledge Refinement via Rule Selection	In several different applications, including data transformation and entity resolution, rules are used to capture aspects of knowledge about the application at hand. Often, a large set of such rules is generated automatically or semi-automatically, and the challenge is to refine the encapsulated knowledge by selecting ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	119,909
2206.02158	Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training	Adversarial training has been widely explored for mitigating attacks against deep models. However, most existing works are still trapped in the dilemma between higher accuracy and stronger robustness since they tend to fit a model towards robust features (not easily tampered with by adversaries) while ignoring those no...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	300,774
1804.02085	Performance Evaluation of 3D Correspondence Grouping Algorithms	This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences. A good correspondence grouping algorithm is desired to retrieve as many as inliers from initial feature matches, giving a rise...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	94,337

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