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
2409.11604	Context-Generative Default Policy for Bounded Rational Agent	Bounded rational agents often make decisions by evaluating a finite selection of choices, typically derived from a reference point termed the $`$default policy,' based on previous experience. However, the inherent rigidity of the static default policy presents significant challenges for agents when operating in unknown...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	489,230
1901.10698	Online Pandora's Boxes and Bandits	We consider online variations of the Pandora's box problem (Weitzman. 1979), a standard model for understanding issues related to the cost of acquiring information for decision-making. Our problem generalizes both the classic Pandora's box problem and the prophet inequality framework. Boxes are presented online, each w...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	120,086
2006.10385	Topology synthesis of a 3-kink contact-aided compliant switch	A topology synthesis approach to design 2D Contact-aided Compliant Mechanisms (CCMs) to trace output paths with three or more kinks is presented. Synthesis process uses three different types of external, rigid contact surfaces: circular, elliptical and rectangular: which in combination, offer intricate local curvatures...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	182,869
2105.08666	Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization	Reinforcement learning (RL) has demonstrated impressive performance in decision-making tasks like embodied control, autonomous driving and financial trading. In many decision-making tasks, the agents often encounter the problem of executing actions under limited budgets. However, classic RL methods typically overlook t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	235,834
1910.13302	Weighted boxes fusion: Ensembling boxes from different object detection models	In this work, we present a novel method for combining predictions of object detection models: weighted boxes fusion. Our algorithm utilizes confidence scores of all proposed bounding boxes to constructs the averaged boxes. We tested method on several datasets and evaluated it in the context of the Open Images and COCO ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	151,357
1207.6313	A CLT on the SNR of Diagonally Loaded MVDR Filters	This paper studies the fluctuations of the signal-to-noise ratio (SNR) of minimum variance distorsionless response (MVDR) filters implementing diagonal loading in the estimation of the covariance matrix. Previous results in the signal processing literature are generalized and extended by considering both spatially as w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,780
2501.18512	Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch	Training of large language models (LLMs) is typically distributed across a large number of accelerators to reduce training time. Since internal states and parameter gradients need to be exchanged at each and every single gradient step, all devices need to be co-located using low-latency high-bandwidth communication lin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	528,732
2209.02470	Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation	Cardiac Magnetic Resonance Imaging is commonly used for the assessment of the cardiac anatomy and function. The delineations of left and right ventricle blood pools and left ventricular myocardium are important for the diagnosis of cardiac diseases. Unfortunately, the movement of a patient during the CMR acquisition pr...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	316,224
2311.12983	GAIA: a benchmark for General AI Assistants	We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. GAIA questions are conceptual...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	409,574
2002.10591	Deep learning predicts total knee replacement from magnetic resonance images	Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow OA progression, but at later stages, only an invasive option is available: total knee replacement (TKR). Though a generally successful pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	165,449
1305.0205	The effect of the initial network configuration on preferential attachment	The classical preferential attachment model is sensitive to the choice of the initial configuration of the network. As the number of initial nodes and their degree grow, so does the time needed for an equilibrium degree distribution to be established. We study this phenomenon, provide estimates of the equilibration tim...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	24,333
2110.00434	Towards Protecting Face Embeddings in Mobile Face Verification Scenarios	This paper proposes PolyProtect, a method for protecting the sensitive face embeddings that are used to represent people's faces in neural-network-based face verification systems. PolyProtect transforms a face embedding to a more secure template, using a mapping based on multivariate polynomials parameterised by user-s...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	258,385
2311.08949	Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard	The volume-corrected mitotic index (M/V-Index) was shown to provide prognostic value in invasive breast carcinomas. However, despite its prognostic significance, it is not established as the standard method for assessing aggressive biological behaviour, due to the high additional workload associated with determining th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	407,929
1901.10968	Bootstrapping Robotic Ecological Perception from a Limited Set of Hypotheses Through Interactive Perception	To solve its task, a robot needs to have the ability to interpret its perceptions. In vision, this interpretation is particularly difficult and relies on the understanding of the structure of the scene, at least to the extent of its task and sensorimotor abilities. A robot with the ability to build and adapt this inter...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	120,145
2110.13583	Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme	Recent research in non-intrusive data-driven model order reduction (MOR) enabled accurate and efficient approximation of parameterized ordinary differential equations (ODEs). However, previous studies have focused on constant parameters, whereas time-dependent parameters have been neglected. The purpose of this paper i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,237
2202.11459	Biometric security technology	This paper presents an overview of the main topics related to biometric security technology, with the main purpose to provide a primer on this subject. Biometrics can offer greater security and convenience than traditional methods for people recognition. Even if we do not want to replace a classic method (password or h...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	281,898
2211.14304	BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction	Stochastic human motion prediction (HMP) has generally been tackled with generative adversarial networks and variational autoencoders. Most prior works aim at predicting highly diverse movements in terms of the skeleton joints' dispersion. This has led to methods predicting fast and motion-divergent movements, which ar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	332,776
1905.05319	Study of Channel Estimation with Oversampling for 1-bit Large-Scale MIMO Systems	In this paper, we propose an oversampling based low-resolution aware least squares channel estimator for large-scale multiple-antenna systems with 1-bit analog-to-digital converters on each receive antenna. To mitigate the information loss caused by the coarse quantization, oversampling is applied at the receiver, wher...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	130,687
2501.11305	Generalizable Spectral Embedding with an Application to UMAP	Spectral Embedding (SE) is a popular method for dimensionality reduction, applicable across diverse domains. Nevertheless, its current implementations face three prominent drawbacks which curtail its broader applicability: generalizability (i.e., out-of-sample extension), scalability, and eigenvectors separation. In th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	525,876
2007.08097	TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris	This paper presents TrashCan, a large dataset comprised of images of underwater trash collected from a variety of sources, annotated both using bounding boxes and segmentation labels, for development of robust detectors of marine debris. The dataset has two versions, TrashCan-Material and TrashCan-Instance, correspondi...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	187,523
2103.10526	S3M: Siamese Stack (Trace) Similarity Measure	Automatic crash reporting systems have become a de-facto standard in software development. These systems monitor target software, and if a crash occurs they send details to a backend application. Later on, these reports are aggregated and used in the development process to 1) understand whether it is a new or an existi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	225,481
1708.00232	Pulse-Based Control Using Koopman Operator Under Parametric Uncertainty	In applications, such as biomedicine and systems/synthetic biology, technical limitations in actuation complicate implementation of time-varying control signals. In order to alleviate some of these limitations, it may be desirable to derive simple control policies, such as step functions with fixed magnitude and length...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	78,180
2404.03605	Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularization	We consider the problem of accurate quantization for language models, where both the weights and activations are uniformly quantized to 4 bits per parameter, the lowest bitwidth format natively supported by GPU hardware. In this context, the key challenge is activation quantization: it is known that language models con...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	444,323
2401.04422	Estimating Text Similarity based on Semantic Concept Embeddings	Due to their ease of use and high accuracy, Word2Vec (W2V) word embeddings enjoy great success in the semantic representation of words, sentences, and whole documents as well as for semantic similarity estimation. However, they have the shortcoming that they are directly extracted from a surface representation, which d...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	420,426
2008.09209	Addestramento con Dataset Sbilanciati	English. The following document pursues the objective of comparing some useful methods to balance a dataset and obtain a trained model. The dataset used for training is made up of short and medium length sentences, such as simple phrases or extracts from conversations that took place on web channels. The training of th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	192,642
1512.07143	SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation	While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningf...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	50,392
2302.08508	Sanity checks and improvements for patch visualisation in prototype-based image classification	In this work, we perform an in-depth analysis of the visualisation methods implemented in two popular self-explaining models for visual classification based on prototypes - ProtoPNet and ProtoTree. Using two fine-grained datasets (CUB-200-2011 and Stanford Cars), we first show that such methods do not correctly identif...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	346,074
1507.04822	Subspace selection for projection maximization with matroid constraints	Suppose that there is a ground set which consists of a large number of vectors in a Hilbert space. Consider the problem of selecting a subset of the ground set such that the projection of a vector of interest onto the subspace spanned by the vectors in the chosen subset reaches the maximum norm. This problem is general...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,219
2310.02074	ACE: A fast, skillful learned global atmospheric model for climate prediction	Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model. The formul...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	396,701
2212.03530	Curiosity creates Diversity in Policy Search	When searching for policies, reward-sparse environments often lack sufficient information about which behaviors to improve upon or avoid. In such environments, the policy search process is bound to blindly search for reward-yielding transitions and no early reward can bias this search in one direction or another. A way...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	335,158
2203.14888	WawPart: Workload-Aware Partitioning of Knowledge Graphs	Large-scale datasets in the form of knowledge graphs are often used in numerous domains, today. A knowledge graphs size often exceeds the capacity of a single computer system, especially if the graph must be stored in main memory. To overcome this, knowledge graphs can be partitioned into multiple sub-graphs and distri...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	true	288,162
2212.00597	When is Cognitive Radar Beneficial?	When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy? We seek insight regarding this question by examining a dynamic spectrum access scenario, in which the radar wishes to transmit in the widest unoccupied bandwidt...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	334,119
2212.07282	Low-Variance Forward Gradients using Direct Feedback Alignment and Momentum	Supervised learning in deep neural networks is commonly performed using error backpropagation. However, the sequential propagation of errors during the backward pass limits its scalability and applicability to low-powered neuromorphic hardware. Therefore, there is growing interest in finding local alternatives to backp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	336,358
1507.05648	Lyapunov-based sufficient conditions for stability of hybrid systems with memory	Hybrid systems with memory are dynamical systems exhibiting both hybrid and delay phenomena. In this note, we study the asymptotic stability of hybrid systems with memory using generalized concepts of solutions. These generalized solutions, motivated by studying robustness and well-posedness of such systems, are define...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	45,307
2102.08358	Efficient Competitions and Online Learning with Strategic Forecasters	Winner-take-all competitions in forecasting and machine-learning suffer from distorted incentives. Witkowski et al. 2018 identified this problem and proposed ELF, a truthful mechanism to select a winner. We show that, from a pool of $n$ forecasters, ELF requires $\Theta(n\log n)$ events or test data points to select a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	220,424
2006.03503	Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape Exploration	The generative adversarial imitation learning (GAIL) has provided an adversarial learning framework for imitating expert policy from demonstrations in high-dimensional continuous tasks. However, almost all GAIL and its extensions only design a kind of reward function of logarithmic form in the adversarial training stra...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	180,332
2406.02365	Exploiting Chordal Sparsity for Fast Global Optimality with Application to Localization	In recent years, many estimation problems in robotics have been shown to be solvable to global optimality using their semidefinite relaxations. However, the runtime complexity of off-the-shelf semidefinite programming (SDP) solvers is up to cubic in problem size, which inhibits real-time solutions of problems involving...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	460,742
1707.08369	Updating Singular Value Decomposition for Rank One Matrix Perturbation	An efficient Singular Value Decomposition (SVD) algorithm is an important tool for distributed and streaming computation in big data problems. It is observed that update of singular vectors of a rank-1 perturbed matrix is similar to a Cauchy matrix-vector product. With this observation, in this paper, we present an eff...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	77,818
2003.05746	Querying and Repairing Inconsistent Prioritized Knowledge Bases: Complexity Analysis and Links with Abstract Argumentation	In this paper, we explore the issue of inconsistency handling over prioritized knowledge bases (KBs), which consist of an ontology, a set of facts, and a priority relation between conflicting facts. In the database setting, a closely related scenario has been studied and led to the definition of three different notions...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	true	167,941
2309.11071	InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Update	Classic Graph Neural Network (GNN) inference approaches, designed for static graphs, are ill-suited for streaming graphs that evolve with time. The dynamism intrinsic to streaming graphs necessitates constant updates, posing unique challenges to acceleration on GPU. We address these challenges based on two key insights...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	393,266
1910.08883	Universally Consistent K-Sample Tests via Dependence Measures	The K-sample testing problem involves determining whether K groups of data points are each drawn from the same distribution. Analysis of variance is arguably the most classical method to test mean differences, along with several recent methods to test distributional differences. In this paper, we demonstrate the existe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	150,000
2302.00988	HaMuCo: Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning	Recent advancements in 3D hand pose estimation have shown promising results, but its effectiveness has primarily relied on the availability of large-scale annotated datasets, the creation of which is a laborious and costly process. To alleviate the label-hungry limitation, we propose a self-supervised learning framewor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	343,438
1506.07656	DeepMatching: Hierarchical Deformable Dense Matching	We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images. DeepMatching relies on a hierarchical, multi-layer, correlational architecture designed for matching images and was inspired by deep convolutional approaches. The proposed matching algorithm can handle non-rig...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	44,548
2403.18209	Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving	Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are d...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	441,822
1409.0964	Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features	This paper aims at constructing a good graph for discovering intrinsic data structures in a semi-supervised learning setting. Firstly, we propose to build a non-negative low-rank and sparse (referred to as NNLRS) graph for the given data representation. Specifically, the weights of edges in the graph are obtained by se...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	35,785
1609.07955	How do walkers avoid a mobile robot crossing their way?	Robots and Humans have to share the same environment more and more often. In the aim of steering robots in a safe and convenient manner among humans it is required to understand how humans interact with them. This work focuses on collision avoidance between a human and a robot during locomotion. Having in mind previous...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	61,512
2402.16667	RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation	Generative models have demonstrated considerable potential in software engineering, particularly in tasks such as code generation and debugging. However, their utilization in the domain of code documentation generation remains underexplored. To this end, we introduce RepoAgent, a large language model powered open-sourc...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	432,647
2302.12093	Experimenting under Stochastic Congestion	We study randomized experiments in a service system when stochastic congestion can arise from temporarily limited supply or excess demand. Such congestion gives rise to cross-unit interference between the waiting customers, and analytic strategies that do not account for this interference may be biased. In current prac...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	347,433
2312.03035	SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction	Sketching is a powerful tool for creating abstract images that are sparse but meaningful. Sketch understanding poses fundamental challenges for general-purpose vision algorithms because it requires robustness to the sparsity of sketches relative to natural visual inputs and because it demands tolerance for semantic amb...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	413,110
2403.02171	Predicting large scale cosmological structure evolution with GAN-based autoencoders	Cosmological simulations play a key role in the prediction and understanding of large scale structure formation from initial conditions. We make use of GAN-based Autoencoders (AEs) in an attempt to predict structure evolution within simulations. The AEs are trained on images and cubes issued from respectively 2D and 3D...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	434,715
1711.08132	Locally Smoothed Neural Networks	Convolutional Neural Networks (CNN) and the locally connected layer are limited in capturing the importance and relations of different local receptive fields, which are often crucial for tasks such as face verification, visual question answering, and word sequence prediction. To tackle the issue, we propose a novel loc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	85,138
2210.12872	Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics	Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model. B...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	325,944
2304.03950	GANHead: Towards Generative Animatable Neural Head Avatars	To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars. This task is challenging, and it is difficult for existing methods to satisfy all the requirements at once. To achieve these goals, we propose GANHead (Generative Animatable Neur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	357,007
0705.3099	Distortion Minimization in Gaussian Layered Broadcast Coding with Successive Refinement	A transmitter without channel state information (CSI) wishes to send a delay-limited Gaussian source over a slowly fading channel. The source is coded in superimposed layers, with each layer successively refining the description in the previous one. The receiver decodes the layers that are supported by the channel real...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	272
1501.07033	Error Correction for Differential Linear Network Coding in Slowly-Varying Networks	Differential linear network coding (DLNC) is a precoding scheme for information transmission over random linear networks. By using differential encoding and decoding, the conventional approach of lifting, required for inherent channel sounding, can be omitted and in turn higher transmission rates are supported. However...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	39,672
2210.02406	Decomposed Prompting: A Modular Approach for Solving Complex Tasks	Few-shot prompting is a surprisingly powerful way to use Large Language Models (LLMs) to solve various tasks. However, this approach struggles as the task complexity increases or when the individual reasoning steps of the task themselves are hard to learn, especially when embedded in more complex tasks. To address this...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	321,637
2003.08061	Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing	Face anti-spoofing is critical to the security of face recognition systems. Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing. Despite the great success, most previous works still formulate the problem as a single-frame multi-task one by simply augmenting the loss wit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,627
1906.02644	An Optimal Control Framework for Online Job Scheduling with General Cost Functions	We consider the problem of online job scheduling on a single machine or multiple unrelated machines with general job/machine-dependent cost functions. In this model, each job $j$ has a processing requirement (length) $v_{ij}$ and arrives with a nonnegative nondecreasing cost function $g_{ij}(t)$ if it has been dispatch...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	134,132
2107.10806	Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI	Prostate cancer (PCa) is the second most common cancer diagnosed among men worldwide. The current PCa diagnostic pathway comes at the cost of substantial overdiagnosis, leading to unnecessary treatment and further testing. Bi-parametric magnetic resonance imaging (bp-MRI) based on apparent diffusion coefficient maps (A...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	247,399
2211.02519	BERT for Long Documents: A Case Study of Automated ICD Coding	Transformer models have achieved great success across many NLP problems. However, previous studies in automated ICD coding concluded that these models fail to outperform some of the earlier solutions such as CNN-based models. In this paper we challenge this conclusion. We present a simple and scalable method to process...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	328,602
2210.13692	Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings	The problem of sequentially maximizing the expectation of a function seeks to maximize the expected value of a function of interest without having direct control on its features. Instead, the distribution of such features depends on a given context and an action taken by an agent. In contrast to Bayesian optimization, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	326,261
0812.2388	Physics of risk and uncertainty in quantum decision making	The Quantum Decision Theory, developed recently by the authors, is applied to clarify the role of risk and uncertainty in decision making and in particular in relation to the phenomenon of dynamic inconsistency. By formulating this notion in precise mathematical terms, we distinguish three types of inconsistency: time ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	2,786
2308.16415	Knowledge Distillation from Non-streaming to Streaming ASR Encoder using Auxiliary Non-streaming Layer	Streaming automatic speech recognition (ASR) models are restricted from accessing future context, which results in worse performance compared to the non-streaming models. To improve the performance of streaming ASR, knowledge distillation (KD) from the non-streaming to streaming model has been studied, mainly focusing ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	388,975
1604.06187	Evolutionary Image Transition Based on Theoretical Insights of Random Processes	Evolutionary algorithms have been widely studied from a theoretical perspective. In particular, the area of runtime analysis has contributed significantly to a theoretical understanding and provided insights into the working behaviour of these algorithms. We study how these insights into evolutionary processes can be u...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	54,909
1604.01171	Sparse Recovery from Extreme Eigenvalues Deviation Inequalities	This article provides a new toolbox to derive sparse recovery guarantees from small deviations on extreme singular values or extreme eigenvalues obtained in Random Matrix Theory. This work is based on Restricted Isometry Constants (RICs) which are a pivotal notion in Compressed Sensing and High-Dimensional Statistics a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	54,157
1808.04285	Unsupervised Hard Example Mining from Videos for Improved Object Detection	Important gains have recently been obtained in object detection by using training objectives that focus on {\em hard negative} examples, i.e., negative examples that are currently rated as positive or ambiguous by the detector. These examples can strongly influence parameters when the network is trained to correct them...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	105,103
2005.09059	Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation	People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant chal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	177,800
2407.00828	DRL-Based RAT Selection in a Hybrid Vehicular Communication Network	Cooperative intelligent transport systems rely on a set of Vehicle-to-Everything (V2X) applications to enhance road safety. Emerging new V2X applications like Advanced Driver Assistance Systems (ADASs) and Connected Autonomous Driving (CAD) applications depend on a significant amount of shared data and require high rel...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	469,024
1908.07748	RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs	Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications. However, current BCNNs are not able to fully explore their corresponding full-precision models, causing a significant per...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	142,363
1005.5581	Multi-View Active Learning in the Non-Realizable Case	The sample complexity of active learning under the realizability assumption has been well-studied. The realizability assumption, however, rarely holds in practice. In this paper, we theoretically characterize the sample complexity of active learning in the non-realizable case under multi-view setting. We prove that, wi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	6,616
2004.09491	On Non-Elitist Evolutionary Algorithms Optimizing Fitness Functions with a Plateau	We consider the expected runtime of non-elitist evolutionary algorithms (EAs), when they are applied to a family of fitness functions with a plateau of second-best fitness in a Hamming ball of radius r around a unique global optimum. On one hand, using the level-based theorems, we obtain polynomial upper bounds on the ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	173,356
2402.13338	Incentivized Exploration via Filtered Posterior Sampling	We study "incentivized exploration" (IE) in social learning problems where the principal (a recommendation algorithm) can leverage information asymmetry to incentivize sequentially-arriving agents to take exploratory actions. We identify posterior sampling, an algorithmic approach that is well known in the multi-armed ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	431,203
1901.10436	Diversity in Faces	Face recognition is a long standing challenge in the field of Artificial Intelligence (AI). The goal is to create systems that accurately detect, recognize, verify, and understand human faces. There are significant technical hurdles in making these systems accurate, particularly in unconstrained settings due to confoun...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	120,020
1301.2030	The One-Bit Null Space Learning Algorithm and its Convergence	This paper proposes a new algorithm for MIMO cognitive radio Secondary Users (SU) to learn the null space of the interference channel to the Primary User (PU) without burdening the PU with any knowledge or explicit cooperation with the SU. The knowledge of this null space enables the SU to transmit in the same band s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,903
2305.10937	The generalized Hierarchical Gaussian Filter	Hierarchical Bayesian models of perception and learning feature prominently in contemporary cognitive neuroscience where, for example, they inform computational concepts of mental disorders. This includes predictive coding and hierarchical Gaussian filtering (HGF), which differ in the nature of hierarchical representat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	365,300
2202.00975	VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering	Sparse linear prediction methods suffer from decreased prediction accuracy when the predictor variables have cluster structure (e.g. there are highly correlated groups of variables). To improve prediction accuracy, various methods have been proposed to identify variable clusters from the data and integrate cluster info...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,329
1601.06763	Emerging Dimension Weights in a Conceptual Spaces Model of Concept Combination	We investigate the generation of new concepts from combinations of properties as an artificial language develops. To do so, we have developed a new framework for conjunctive concept combination. This framework gives a semantic grounding to the weighted sum approach to concept combination seen in the literature. We impl...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	51,338
1801.02730	Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data	On image data, data augmentation is becoming less relevant due to the large amount of available training data and regularization techniques. Common approaches are moving windows (cropping), scaling, affine distortions, random noise, and elastic deformations. For electroencephalographic data, the lack of sufficient trai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	87,973
1602.07630	Online Dual Coordinate Ascent Learning	The stochastic dual coordinate-ascent (S-DCA) technique is a useful alternative to the traditional stochastic gradient-descent algorithm for solving large-scale optimization problems due to its scalability to large data sets and strong theoretical guarantees. However, the available S-DCA formulation is limited to finit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	52,541
2211.11492	ClipCrop: Conditioned Cropping Driven by Vision-Language Model	Image cropping has progressed tremendously under the data-driven paradigm. However, current approaches do not account for the intentions of the user, which is an issue especially when the composition of the input image is complex. Moreover, labeling of cropping data is costly and hence the amount of data is limited, le...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	331,752
2304.07744	JoB-VS: Joint Brain-Vessel Segmentation in TOF-MRA Images	We propose the first joint-task learning framework for brain and vessel segmentation (JoB-VS) from Time-of-Flight Magnetic Resonance images. Unlike state-of-the-art vessel segmentation methods, our approach avoids the pre-processing step of implementing a model to extract the brain from the volumetric input data. Skipp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	358,459
2211.16838	Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation	Reinforcement Learning has emerged as a strong alternative to solve optimization tasks efficiently. The use of these algorithms highly depends on the feedback signals provided by the environment in charge of informing about how good (or bad) the decisions made by the learned agent are. Unfortunately, in a broad range o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	333,769
1907.07352	Dynamic Malware Analysis with Feature Engineering and Feature Learning	Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released ("zero-day") malware. However, existing works typicall...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	138,857
2007.06672	Landslide Segmentation with U-Net: Evaluating Different Sampling Methods and Patch Sizes	Landslide inventory maps are crucial to validate predictive landslide models; however, since most mapping methods rely on visual interpretation or expert knowledge, detailed inventory maps are still lacking. This study used a fully convolutional deep learning model named U-net to automatically segment landslides in the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,079
1709.08518	A View-Dependent Adaptive Matched Filter for LADAR-Based Vehicle Tracking	LADARs mounted on mobile platforms produce a wealth of precise range data on the surrounding objects and vehicles. The challenge we address is to infer from these raw LADAR data the location and orientation of nearby vehicles. We propose a novel view-dependent adaptive matched filter for obtaining fast and precise meas...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	81,486
2307.05209	Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning	Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes. To expedite learning when transferring to unseen tasks, we propose a novel approach to representing the current task using reward machines (RMs), state...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	378,660
2407.17312	Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches	Adversarial attacks against monocular depth estimation (MDE) systems pose significant challenges, particularly in safety-critical applications such as autonomous driving. Existing patch-based adversarial attacks for MDE are confined to the vicinity of the patch, making it difficult to affect the entire target. To addre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	475,918
2202.12843	Dynamic Regret of Online Mirror Descent for Relatively Smooth Convex Cost Functions	The performance of online convex optimization algorithms in a dynamic environment is often expressed in terms of the dynamic regret, which measures the decision maker's performance against a sequence of time-varying comparators. In the analysis of the dynamic regret, prior works often assume Lipschitz continuity or uni...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,381
2412.10908	Do large language vision models understand 3D shapes?	Large vision language models (LVLM) are the leading A.I approach for achieving a general visual understanding of the world. Models such as GPT, Claude, Gemini, and LLama can use images to understand and analyze complex visual scenes. 3D objects and shapes are the basic building blocks of the world, recognizing them is ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	517,162
2012.09398	Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation	We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,060
1806.01217	Efficient Genomic Interval Queries Using Augmented Range Trees	Efficient large-scale annotation of genomic intervals is essential for personal genome interpretation in the realm of precision medicine. There are 13 possible relations between two intervals according to Allen's interval algebra. Conventional interval trees are routinely used to identify the genomic intervals satisfyi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	99,503
2203.04886	Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees	Deep neural network-based classifiers have been shown to be vulnerable to imperceptible perturbations to their input, such as $\ell_p$-bounded norm adversarial attacks. This has motivated the development of many defense methods, which are then broken by new attacks, and so on. This paper focuses on a different but rela...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	284,636
2302.00704	Pathologies of Predictive Diversity in Deep Ensembles	Classic results establish that encouraging predictive diversity improves performance in ensembles of low-capacity models, e.g. through bagging or boosting. Here we demonstrate that these intuitions do not apply to high-capacity neural network ensembles (deep ensembles), and in fact the opposite is often true. In a larg...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	343,309
2009.08997	Psoriasis Severity Assessment with a Similarity-Clustering Machine Learning Approach Reduces Intra- and Inter-observation variation	Psoriasis is a complex disease with many variations in genotype and phenotype. General advancements in medicine has further complicated both assessments and treatment for both physicians and dermatologist alike. Even with all of our technological progress we still primarily use the assessment tool Psoriasis Area and Se...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	196,414
2201.10838	Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant	Training logistic regression over encrypted data has been a compelling approach in addressing security concerns for several years. In this paper, we introduce an efficient gradient variant, called $quadratic$ $gradient$, for privacy-preserving logistic regression training. We enhance Nesterov's Accelerated Gradient (NA...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	277,118
2405.07097	Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systems	This paper explores the efficacy of diffusion-based generative models as neural operators for partial differential equations (PDEs). Neural operators are neural networks that learn a mapping from the parameter space to the solution space of PDEs from data, and they can also solve the inverse problem of estimating the p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	453,578
1408.5928	Unicast Barrage Relay Networks: Outage Analysis and Optimization	Barrage relays networks (BRNs) are ad hoc networks built on a rapid cooperative flooding primitive as opposed to the traditional point-to-point link abstraction. Controlled barrage regions (CBRs) can be used to contain this flooding primitive for unicast and multicast, thereby enabling spatial reuse. In this paper, the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	35,591
2110.04422	Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation	Recently, deep reinforcement learning (RL) has achieved remarkable empirical success by integrating deep neural networks into RL frameworks. However, these algorithms often require a large number of training samples and admit little theoretical understanding. To mitigate these issues, we propose a theoretically princip...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	259,879
1909.06322	A Knowledge Transfer Framework for Differentially Private Sparse Learning	We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example. We develop a differentially private high-dimensional sparse learning framework using the idea of knowledge transfer. More specifically, we propose to distill the knowledge ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	145,345
2311.08535	Taxonomy, Semantic Data Schema, and Schema Alignment for Open Data in Urban Building Energy Modeling	Urban Building Energy Modeling (UBEM) is a critical tool to provide quantitative analysis on building decarbonization, sustainability, building-to-grid integration, and renewable energy applications on city, regional, and national scales. Researchers usually use open data as inputs to build and calibrate UBEM. However,...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	407,761

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