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
2501.03151	Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches	Generative artificial intelligence (AI) systems based on large-scale pretrained foundation models (PFMs) such as vision-language models, large language models (LLMs), diffusion models and vision-language-action (VLA) models have demonstrated the ability to solve complex and truly non-trivial AI problems in a wide varie...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	522,775
2302.09176	Generative Ornstein-Uhlenbeck Markets via Geometric Deep Learning	We consider the problem of simultaneously approximating the conditional distribution of market prices and their log returns with a single machine learning model. We show that an instance of the GDN model of Kratsios and Papon (2022) solves this problem without having prior assumptions on the market's "clipped" log retu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	346,309
2112.13396	Energy-Efficient Trajectory Design for UAV-Aided Maritime Data Collection in Wind	Unmanned aerial vehicles (UAVs), especially fixed-wing ones that withstand strong winds, have great potential for oceanic exploration and research. This paper studies a UAV-aided maritime data collection system with a fixed-wing UAV dispatched to collect data from marine buoys. We aim to minimize the UAV's energy consu...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	273,230
2104.14963	Determining Chess Game State From an Image	Identifying the configuration of chess pieces from an image of a chessboard is a problem in computer vision that has not yet been solved accurately. However, it is important for helping amateur chess players improve their games by facilitating automatic computer analysis without the overhead of manually entering the pi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	233,011
2306.05101	Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning	We introduce a novel Pseudo-Negative Regularization (PNR) framework for effective continual self-supervised learning (CSSL). Our PNR leverages pseudo-negatives obtained through model-based augmentation in a way that newly learned representations may not contradict what has been learned in the past. Specifically, for th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	372,053
2207.11321	A flexible PageRank-based graph embedding framework closely related to spectral eigenvector embeddings	We study a simple embedding technique based on a matrix of personalized PageRank vectors seeded on a random set of nodes. We show that the embedding produced by the element-wise logarithm of this matrix (1) are related to the spectral embedding for a class of graphs where spectral embeddings are significant, and hence ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	309,587
1309.7937	Stationary Cycling Induced by Switched Functional Electrical Stimulation Control	Functional electrical stimulation (FES) is used to activate the dysfunctional lower limb muscles of individuals with neuromuscular disorders to produce cycling as a means of exercise and rehabilitation. However, FES-cycling is still metabolically inefficient and yields low power output at the cycle crank compared to ab...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	27,435
2403.19365	EthioMT: Parallel Corpus for Low-resource Ethiopian Languages	Recent research in natural language processing (NLP) has achieved impressive performance in tasks such as machine translation (MT), news classification, and question-answering in high-resource languages. However, the performance of MT leaves much to be desired for low-resource languages. This is due to the smaller size...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	442,315
2007.07698	Are Hyperbolic Representations in Graphs Created Equal?	Recently there was an increasing interest in applications of graph neural networks in non-Euclidean geometry; however, are non-Euclidean representations always useful for graph learning tasks? For different problems such as node classification and link prediction we compute hyperbolic embeddings and conclude that for t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	187,413
1808.04547	Machine Learning for Heterogeneous Ultra-Dense Networks with Graphical Representations	Heterogeneous ultra-dense network (H-UDN) is envisioned as a promising solution to sustain the explosive mobile traffic demand through network densification. By placing access points, processors, and storage units as close as possible to mobile users, H-UDNs bring forth a number of advantages, including high spectral e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	105,178
2004.07995	A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation	Deep learning based image segmentation has achieved the state-of-the-art performance in many medical applications such as lesion quantification, organ detection, etc. However, most of the methods rely on supervised learning, which require a large set of high-quality labeled data. Data annotation is generally an extreme...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	172,928
2209.01279	Distributed Interval Observers for Bounded-Error LTI Systems	This paper proposes a novel distributed interval observer design for linear time-invariant (LTI) discrete-time systems subject to bounded disturbances. In the proposed observer algorithm, each agent in a networked group exchanges locally-computed framers or interval-valued state estimates with neighbors, and coordinate...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	315,824
2111.13921	Transformed K-means Clustering	In this work we propose a clustering framework based on the paradigm of transform learning. In simple terms the representation from transform learning is used for K-means clustering; however, the problem is not solved in such a na\"ive piecemeal fashion. The K-means clustering loss is embedded into the transform learni...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	268,429
2402.12366	A Critical Evaluation of AI Feedback for Aligning Large Language Models	Reinforcement learning with AI feedback (RLAIF) is a popular paradigm for improving the instruction-following abilities of powerful pre-trained language models. RLAIF first performs supervised fine-tuning (SFT) using demonstrations from a teacher model and then further fine-tunes the model with reinforcement learning (...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	430,817
2103.08721	A Central Limit Theorem for Differentially Private Query Answering	Perhaps the single most important use case for differential privacy is to privately answer numerical queries, which is usually achieved by adding noise to the answer vector. The central question, therefore, is to understand which noise distribution optimizes the privacy-accuracy trade-off, especially when the dimension...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	224,968
2209.07663	Monolith: Real Time Recommendation System With Collisionless Embedding Table	Building a scalable and real-time recommendation system is vital for many businesses driven by time-sensitive customer feedback, such as short-videos ranking or online ads. Despite the ubiquitous adoption of production-scale deep learning frameworks like TensorFlow or PyTorch, these general-purpose frameworks fall shor...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	317,836
2404.07940	InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models	Large Language Models for code (code LLMs) have witnessed tremendous progress in recent years. With the rapid development of code LLMs, many popular evaluation benchmarks, such as HumanEval, DS-1000, and MBPP, have emerged to measure the performance of code LLMs with a particular focus on code generation tasks. However...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	446,029
1806.06957	A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation	Recently, neural machine translation (NMT) has been extended to multilinguality, that is to handle more than one translation direction with a single system. Multilingual NMT showed competitive performance against pure bilingual systems. Notably, in low-resource settings, it proved to work effectively and efficiently, t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	100,801
2101.06355	Rapid Method for Generation Prioritization during System Restoration with Renewable Resources	Quick and reliable power system restoration is critically important after natural disasters or other sudden threats, such as cyber-attacks. Leveraging renewable resources in system restoration shortens recovery times, resulting in prevented life-loss and avoided economic-loss, and improves the resilience of the entire ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	215,683
2006.09785	Self-supervised Knowledge Distillation for Few-shot Learning	Real-world contains an overwhelmingly large number of object classes, learning all of which at once is infeasible. Few shot learning is a promising learning paradigm due to its ability to learn out of order distributions quickly with only a few samples. Recent works [7, 41] show that simply learning a good feature embe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	182,653
1911.02736	Analysis of CNN-based remote-PPG to understand limitations and sensitivities	Deep learning based on Convolutional Neural Network (CNN) has shown promising results in various vision-based applications, recently also in camera-based vital signs monitoring. The CNN-based Photoplethysmography (PPG) extraction has, so far, been focused on performance rather than understanding. In this paper, we try ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	152,440
1511.02821	Partial Membership Latent Dirichlet Allocation	Topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting imagery. These models are confined to crisp segmentation. Yet, there are many images in which some regions cannot be assigned a crisp label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	48,681
1908.05848	Sketch-Driven Regular Expression Generation from Natural Language and Examples	Recent systems for converting natural language descriptions into regular expressions (regexes) have achieved some success, but typically deal with short, formulaic text and can only produce simple regexes. Realworld regexes are complex, hard to describe with brief sentences, and sometimes require examples to fully conv...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	141,831
2409.05527	Power Control of Converters Connected via an L Filter to a Weak Grid. A Flatness-Based Approach	In this article, a nonlinear strategy based on a flatness approach is used for controlling the instantaneous complex power supplied from the Point of Common Coupling (PCC) to a weak grid. To this end, the strategy introduced by the authors in [1] considering a strong grid is robustified for avoiding system instability ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	486,807
1403.5645	Transaction Repair: Full Serializability Without Locks	Transaction Repair is a method for lock-free, scalable transaction processing that achieves full serializability. It demonstrates parallel speedup even in inimical scenarios where all pairs of transactions have significant read-write conflicts. In the transaction repair approach, each transaction runs in complete isola...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	31,749
2302.05530	Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specifications	Engineering more secure software has become a critical challenge in the cyber world. It is very important to develop methodologies, techniques, and tools for developing secure software. To develop secure software, software developers need to think like an attacker through mining software repositories. These aim to anal...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	345,077
1801.01875	Near Optimal Coded Data Shuffling for Distributed Learning	Data shuffling between distributed cluster of nodes is one of the critical steps in implementing large-scale learning algorithms. Randomly shuffling the data-set among a cluster of workers allows different nodes to obtain fresh data assignments at each learning epoch. This process has been shown to provide improvements...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	87,818
2109.03464	Level Set Binocular Stereo with Occlusions	Localizing stereo boundaries and predicting nearby disparities are difficult because stereo boundaries induce occluded regions where matching cues are absent. Most modern computer vision algorithms treat occlusions secondarily (e.g., via left-right consistency checks after matching) or rely on high-level cues to improv...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,078
1711.01082	On the Capacity of SWIPT Systems with a Nonlinear Energy Harvesting Circuit	In this paper, we study information-theoretic limits for simultaneous wireless information and power transfer (SWIPT) systems employing a practical nonlinear radio frequency (RF) energy harvesting (EH) receiver. In particular, we consider a three-node system with one transmitter that broadcasts a common signal to separ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	83,831
1611.01626	Combining policy gradient and Q-learning	Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting. However, vanilla online variants are on-policy only and not able to take advantage of off-policy data. In this paper we describe a new technique that combines policy gradient with off-policy Q-learning, drawing experie...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	63,408
2301.12527	Diverse, Difficult, and Odd Instances (D2O): A New Test Set for Object Classification	Test sets are an integral part of evaluating models and gauging progress in object recognition, and more broadly in computer vision and AI. Existing test sets for object recognition, however, suffer from shortcomings such as bias towards the ImageNet characteristics and idiosyncrasies (e.g., ImageNet-V2), being limited...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	342,564
1901.02350	Robust and High Performance Face Detector	In recent years, face detection has experienced significant performance improvement with the boost of deep convolutional neural networks. In this report, we reimplement the state-of-the-art detector SRN and apply some tricks proposed in the recent literatures to obtain an extremely strong face detector, named VIM-FD. I...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,180
2111.12221	Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation	Domain adaptation is crucial for transferring the knowledge from the source labeled CT dataset to the target unlabeled MR dataset in abdominal multi-organ segmentation. Meanwhile, it is highly desirable to avoid the high annotation cost related to the target dataset and protect the source dataset privacy. Therefore, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,903
2407.04489	Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model	Prompt learning methods are gaining increasing attention due to their ability to customize large vision-language models to new domains using pre-trained contextual knowledge and minimal training data. However, existing works typically rely on optimizing unified prompt inputs, often struggling with fine-grained classifi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	470,579
2312.06034	Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flows	Designing predictive models for subjective problems in natural language processing (NLP) remains challenging. This is mainly due to its non-deterministic nature and different perceptions of the content by different humans. It may be solved by Personalized Natural Language Processing (PNLP), where the model exploits add...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	414,341
2103.12337	Salient Image Matting	In this paper, we propose an image matting framework called Salient Image Matting to estimate the per-pixel opacity value of the most salient foreground in an image. To deal with a large amount of semantic diversity in images, a trimap is conventionally required as it provides important guidance about object semantics ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	226,136
2110.10316	Beamforming Design for Intelligent Reflecting Surface-Enhanced Symbiotic Radio Systems	This paper investigates multiuser multi-input single-output downlink symbiotic radio communication systems assisted by an intelligent reflecting surface (IRS). Different from existing methods ideally assuming the secondary user (SU) can jointly decode information symbols from both the access point (AP) and the IRS via ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	262,101
2410.07097	A Law of Large Numbers for SIR on the Stochastic Block Model: A Proof via Herd Immunity	In this paper, we study the dynamics of the susceptible-infected-recovered (SIR) model on a network with community structure, namely the stochastic block model (SBM). As usual, the SIR model is a stochastic model for an epidemic where infected vertices infect susceptible neighbors at some rate $\eta$ and recover at rat...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	496,467
2311.09476	ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems	Evaluating retrieval-augmented generation (RAG) systems traditionally relies on hand annotations for input queries, passages to retrieve, and responses to generate. We introduce ARES, an Automated RAG Evaluation System, for evaluating RAG systems along the dimensions of context relevance, answer faithfulness, and answe...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	408,144
2105.03523	Test Suites as a Source of Training Data for Static Analysis Alert Classifiers	Flaw-finding static analysis tools typically generate large volumes of code flaw alerts including many false positives. To save on human effort to triage these alerts, a significant body of work attempts to use machine learning to classify and prioritize alerts. Identifying a useful set of training data, however, remai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	234,168
1904.03796	Minimum Enclosing Ball Revisited: Stability and Sub-linear Time Algorithms	In this paper, we revisit the Minimum Enclosing Ball (MEB) problem and its robust version, MEB with outliers, in Euclidean space $\mathbb{R}^d$. Though the problem has been extensively studied before, most of the existing algorithms need at least linear time (in the number of input points $n$ and the dimensionality $d$...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	126,836
1911.02150	Fast Transformer Decoding: One Write-Head is All You Need	Multi-head attention layers, as used in the Transformer neural sequence model, are a powerful alternative to RNNs for moving information across and between sequences. While training these layers is generally fast and simple, due to parallelizability across the length of the sequence, incremental inference (where such p...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	152,290
1611.04465	Advancing Memristive Analog Neuromorphic Networks: Increasing Complexity, and Coping with Imperfect Hardware Components	We experimentally demonstrate classification of 4x4 binary images into 4 classes, using a 3-layer mixed-signal neuromorphic network ("MLP perceptron"), based on two passive 20x20 memristive crossbar arrays, board-integrated with discrete CMOS components. The network features 10 hidden-layer and 4 output-layer analog CM...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	63,847
2306.17456	Human-like Decision-making at Unsignalized Intersection using Social Value Orientation	With the commercial application of automated vehicles (AVs), the sharing of roads between AVs and human-driven vehicles (HVs) becomes a common occurrence in the future. While research has focused on improving the safety and reliability of autonomous driving, it's also crucial to consider collaboration between AVs and H...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	376,709
2209.10691	PREF: Predictability Regularized Neural Motion Fields	Knowing the 3D motions in a dynamic scene is essential to many vision applications. Recent progress is mainly focused on estimating the activity of some specific elements like humans. In this paper, we leverage a neural motion field for estimating the motion of all points in a multiview setting. Modeling the motion fro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	318,941
2408.15049	Scalable Supervisory Architecture for Autonomous Race Cars	In recent years, the number and importance of autonomous racing leagues, and consequently the number of studies on them, has been growing. The seamless integration between different series has gained attention due to the scene's diversity. However, the high cost of full scale racing makes it a more accessible developme...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	483,780
2312.09958	Distilling Large Language Models for Matching Patients to Clinical Trials	The recent success of large language models (LLMs) has paved the way for their adoption in the high-stakes domain of healthcare. Specifically, the application of LLMs in patient-trial matching, which involves assessing patient eligibility against clinical trial's nuanced inclusion and exclusion criteria, has shown prom...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	415,946
2006.08679	Feature Space Saturation during Training	We propose layer saturation - a simple, online-computable method for analyzing the information processing in neural networks. First, we show that a layer's output can be restricted to the eigenspace of its variance matrix without performance loss. We propose a computationally lightweight method for approximating the va...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	182,258
1906.04960	Towards Geocoding Spatial Expressions	Imprecise composite location references formed using ad hoc spatial expressions in English text makes the geocoding task challenging for both inference and evaluation. Typically such spatial expressions fill in unestablished areas with new toponyms for finer spatial referents. For example, the spatial extent of the ad ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	134,892
2304.13357	Deep Lifelong Cross-modal Hashing	Hashing methods have made significant progress in cross-modal retrieval tasks with fast query speed and low storage cost. Among them, deep learning-based hashing achieves better performance on large-scale data due to its excellent extraction and representation ability for nonlinear heterogeneous features. However, ther...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	360,549
2308.02569	BioBERT Based SNP-traits Associations Extraction from Biomedical Literature	Scientific literature contains a considerable amount of information that provides an excellent opportunity for developing text mining methods to extract biomedical relationships. An important type of information is the relationship between singular nucleotide polymorphisms (SNP) and traits. In this paper, we present a ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	383,691
2008.07861	Depth Completion with RGB Prior	Depth cameras are a prominent perception system for robotics, especially when operating in natural unstructured environments. Industrial applications, however, typically involve reflective objects under harsh lighting conditions, a challenging scenario for depth cameras, as it induces numerous reflections and deflectio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,240
2304.14520	Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration	Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and Light Detection and Rangings (LiDARs), the performance of which may degrade in the presence of aerosol pa...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	361,004
1902.09835	Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?	World-class human players have been outperformed in a number of complex two person games (Go, Chess, Checkers) by Deep Reinforcement Learning systems. However, owing to tractability considerations minimax regret of a learning system cannot be evaluated in such games. In this paper we consider simple games (Noughts-and-...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	122,518
1904.03501	DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection	Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. It remains challenging to build nodule detection deep learning models with good generalization performance due to unbalanced positive and negative samples. In order to overcome this problem and furt...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,743
1503.06914	A Fundamental Inequality for Lower-bounding the Error Probability for Classical and Quantum Multiple Access Channels and Its Applications	In the study of the capacity problem for multiple access channels (MACs), a lower bound on the error probability obtained by Han plays a crucial role in the converse parts of several kinds of channel coding theorems in the information-spectrum framework. Recently, Yagi and Oohama showed a tighter bound than the Han bou...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	41,416
2108.03022	Utilizing Treewidth for Quantitative Reasoning on Epistemic Logic Programs	Extending the popular Answer Set Programming (ASP) paradigm by introspective reasoning capacities has received increasing interest within the last years. Particular attention is given to the formalism of epistemic logic programs (ELPs) where standard rules are equipped with modal operators which allow to express condit...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	249,533
2406.19396	SimLOB: Learning Representations of Limited Order Book for Financial Market Simulation	Financial market simulation (FMS) serves as a promising tool for understanding market anomalies and the underlying trading behaviors. To ensure high-fidelity simulations, it is crucial to calibrate the FMS model for generating data closely resembling the observed market data. Previous efforts primarily focused on calib...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	468,407
1809.03182	Towards one-shot learning for rare-word translation with external experts	Neural machine translation (NMT) has significantly improved the quality of automatic translation models. One of the main challenges in current systems is the translation of rare words. We present a generic approach to address this weakness by having external models annotate the training data as Experts, and control the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	107,257
2302.02117	Learning to Agree on Vision Attention for Visual Commonsense Reasoning	Visual Commonsense Reasoning (VCR) remains a significant yet challenging research problem in the realm of visual reasoning. A VCR model generally aims at answering a textual question regarding an image, followed by the rationale prediction for the preceding answering process. Though these two processes are sequential a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	343,861
2202.06085	Online V2X Scheduling for Raw-Level Cooperative Perception	Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence. While raw-level cooperative perception preserves most information to guarantee accuracy, it is demanding in communication bandwidth and computation power. Therefore, it is important to schedule th...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	280,089
2303.00694	The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms	We propose a novel approach to addressing two fundamental challenges in Model-based Reinforcement Learning (MBRL): the computational expense of repeatedly finding a good policy in the learned model, and the objective mismatch between model fitting and policy computation. Our "lazy" method leverages a novel unified obje...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	348,682
2302.04730	A Benchmark on Uncertainty Quantification for Deep Learning Prognostics	Reliable uncertainty quantification on RUL prediction is crucial for informative decision-making in predictive maintenance. In this context, we assess some of the latest developments in the field of uncertainty quantification for prognostics deep learning. This includes the state-of-the-art variational inference algori...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	344,798
2107.09158	Improving exploration in policy gradient search: Application to symbolic optimization	Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols. In contrast to traditional evolutionary approaches, using a neural network at the core of the search allows learning higher-level symbolic patterns, providing a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,950
2403.05101	Rule-driven News Captioning	News captioning task aims to generate sentences by describing named entities or concrete events for an image with its news article. Existing methods have achieved remarkable results by relying on the large-scale pre-trained models, which primarily focus on the correlations between the input news content and the output ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	435,868
2411.00627	Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks	Deep neural networks perform well in object recognition, but do they perceive objects like humans? This study investigates the Gestalt principle of closure in convolutional neural networks. We propose a protocol to identify closure and conduct experiments using simple visual stimuli with progressively removed edge sect...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	504,676
2408.10265	Distributed and Secure Kernel-Based Quantum Machine Learning	Quantum computing promises to revolutionize machine learning, offering significant efficiency gains in tasks such as clustering and distance estimation. Additionally, it provides enhanced security through fundamental principles like the measurement postulate and the no-cloning theorem, enabling secure protocols such as...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,786
2302.02407	HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks	Convolutional neural network (CNN) inference using fully homomorphic encryption (FHE) is a promising private inference (PI) solution due to the capability of FHE that enables offloading the whole computation process to the server while protecting the privacy of sensitive user data. Prior FHE-based CNN (HCNN) work has d...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	343,989
2411.18376	Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework	We present SNOWS, a one-shot post-training pruning framework aimed at reducing the cost of vision network inference without retraining. Current leading one-shot pruning methods minimize layer-wise least squares reconstruction error which does not take into account deeper network representations. We propose to optimize ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	511,846
2210.15462	He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues	In this work, we define a new style transfer task: perspective shift, which reframes a dialogue from informal first person to a formal third person rephrasing of the text. This task requires challenging coreference resolution, emotion attribution, and interpretation of informal text. We explore several baseline approac...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	326,965
1812.03527	A Deep Multi-task Learning Approach to Skin Lesion Classification	Skin lesion identification is a key step toward dermatological diagnosis. When describing a skin lesion, it is very important to note its body site distribution as many skin diseases commonly affect particular parts of the body. To exploit the correlation between skin lesions and their body site distributions, in this ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	116,037
2307.11288	Kernelized Offline Contextual Dueling Bandits	Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning from human feedback on large language models. For many of these applications, the cost of acquiring the human feedback can be substantial o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	380,849
1911.11691	Emergent Structures and Lifetime Structure Evolution in Artificial Neural Networks	Motivated by the flexibility of biological neural networks whose connectivity structure changes significantly during their lifetime, we introduce the Unstructured Recursive Network (URN) and demonstrate that it can exhibit similar flexibility during training via gradient descent. We show empirically that many of the di...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	155,198
2108.09523	Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning	Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials. Materials science experts excel at solving simple systems but cannot solve complex systems, creating a major bottleneck in high-throughput ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	251,632
2501.17397	Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domains	Question generation in education is a time-consuming and cognitively demanding task, as it requires creating questions that are both contextually relevant and pedagogically sound. Current automated question generation methods often generate questions that are out of context. In this work, we explore advanced techniques...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	528,323
2402.07330	Expert-Adaptive Medical Image Segmentation	Medical image segmentation (MIS) plays an instrumental role in medical image analysis, where considerable effort has been devoted to automating the process. Currently, mainstream MIS approaches are based on deep neural networks (DNNs), which are typically trained on a dataset with annotations produced by certain medica...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	428,661
2108.02555	DeepScanner: a Robotic System for Automated 2D Object Dataset Collection with Annotations	In the proposed study, we describe the possibility of automated dataset collection using an articulated robot. The proposed technology reduces the number of pixel errors on a polygonal dataset and the time spent on manual labeling of 2D objects. The paper describes a novel automatic dataset collection and annotation sy...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	249,363
2410.05468	PH-Dropout: Practical Epistemic Uncertainty Quantification for View Synthesis	View synthesis using Neural Radiance Fields (NeRF) and Gaussian Splatting (GS) has demonstrated impressive fidelity in rendering real-world scenarios. However, practical methods for accurate and efficient epistemic Uncertainty Quantification (UQ) in view synthesis are lacking. Existing approaches for NeRF either introd...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	495,752
1301.0875	On Event Triggered Tracking for Nonlinear Systems	In this paper we study an event based control algorithm for trajectory tracking in nonlinear systems. The desired trajectory is modelled as the solution of a reference system with an exogenous input and it is assumed that the desired trajectory and the exogenous input to the reference system are uniformly bounded. Give...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	20,810
1410.3987	Model-Free 3D Reconstruction of Weld Joint Using Laser Scanning	This article presents a novel utilization of the concept of entropy in information theory to model-free 3D reconstruction of weld joint in presence of noise. We show that our formulation attains its global minimum at the upper edge of this joint. This property significantly simplifies the extraction of this welding joi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	36,758
1906.01603	Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study	Neural generative models have been become increasingly popular when building conversational agents. They offer flexibility, can be easily adapted to new domains, and require minimal domain engineering. A common criticism of these systems is that they seldom understand or use the available dialog history effectively. In...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	133,771
2106.06174	Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)	The Generalized Moving Peaks Benchmark (GMPB) is a tool for generating continuous dynamic optimization problem instances with controllable dynamic and morphological characteristics. GMPB has been used in recent Competitions on Dynamic Optimization at prestigious conferences, such as the IEEE Congress on Evolutionary Co...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	240,396
2211.03128	Confidence-Ranked Reconstruction of Census Microdata from Published Statistics	A reconstruction attack on a private dataset $D$ takes as input some publicly accessible information about the dataset and produces a list of candidate elements of $D$. We introduce a new class of data reconstruction attacks based on randomized methods for non-convex optimization. We empirically demonstrate that our at...	false	false	false	false	false	false	true	false	false	false	false	false	true	true	false	false	false	false	328,835
2105.03627	Improving Cross-Lingual Reading Comprehension with Self-Training	Substantial improvements have been made in machine reading comprehension, where the machine answers questions based on a given context. Current state-of-the-art models even surpass human performance on several benchmarks. However, their abilities in the cross-lingual scenario are still to be explored. Previous works ha...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	234,205
2305.04724	Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing	In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy, which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the reti...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	362,882
2407.12009	Using Multimodal Foundation Models and Clustering for Improved Style Ambiguity Loss	Teaching text-to-image models to be creative involves using style ambiguity loss, which requires a pretrained classifier. In this work, we explore a new form of the style ambiguity training objective, used to approximate creativity, that does not require training a classifier or even a labeled dataset. We then train a ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	473,718
1407.3926	Strategy Synthesis for General Deductive Games Based on SAT Solving	We propose a general framework for modelling and solving deductive games, where one player selects a secret code and the other player strives to discover this code using a minimal number of allowed experiments that reveal some partial information about the code. The framework is implemented in a software tool Cobra, an...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	34,666
1205.6974	The Porosity of Additive Noise Sequences	Consider a binary additive noise channel with noiseless feedback. When the noise is a stationary and ergodic process $\mathbf{Z}$, the capacity is $1-\mathbb{H}(\mathbf{Z})$ ($\mathbb{H}(\cdot)$ denoting the entropy rate). It is shown analogously that when the noise is a deterministic sequence $z^\infty$, the capacity ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	16,264
1709.05087	Viewpoint Invariant Action Recognition using RGB-D Videos	In video-based action recognition, viewpoint variations often pose major challenges because the same actions can appear different from different views. We use the complementary RGB and Depth information from the RGB-D cameras to address this problem. The proposed technique capitalizes on the spatio-temporal information...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,784
2409.11502	Super Resolution On Global Weather Forecasts	Weather forecasting is a vitally important tool for tasks ranging from planning day to day activities to disaster response planning. However, modeling weather has proven to be challenging task due to its chaotic and unpredictable nature. Each variable, from temperature to precipitation to wind, all influence the path t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	489,179
1910.13276	a novel cross-lingual voice cloning approach with a few text-free samples	In this paper, we present a cross-lingual voice cloning approach. BN features obtained by SI-ASR model are used as a bridge across speakers and language boundaries. The relationships between text and BN features are modeled by the latent prosody model. The acoustic model learns the translation from BN features to acous...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	151,347
2008.04779	ARX Model Identification using Generalized Spectral Decomposition	This article is concerned with the identification of autoregressive with exogenous inputs (ARX) models. Most of the existing approaches like prediction error minimization and state-space framework are widely accepted and utilized for the estimation of ARX models but are known to deliver unbiased and consistent paramete...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	191,320
1806.08593	Tensor Monte Carlo: particle methods for the GPU era	Multi-sample, importance-weighted variational autoencoders (IWAE) give tighter bounds and more accurate uncertainty estimates than variational autoencoders (VAE) trained with a standard single-sample objective. However, IWAEs scale poorly: as the latent dimensionality grows, they require exponentially many samples to r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	101,183
2301.13247	Online Loss Function Learning	Loss function learning is a new meta-learning paradigm that aims to automate the essential task of designing a loss function for a machine learning model. Existing techniques for loss function learning have shown promising results, often improving a model's training dynamics and final inference performance. However, a ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	342,825
2402.14521	Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction	Standard English and Malaysian English exhibit notable differences, posing challenges for natural language processing (NLP) tasks on Malaysian English. Unfortunately, most of the existing datasets are mainly based on standard English and therefore inadequate for improving NLP tasks in Malaysian English. An experiment u...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	431,720
1802.02904	Deep Reinforcement Learning for Image Hashing	Deep hashing methods have received much attention recently, which achieve promising results by taking advantage of the strong representation power of deep networks. However, most existing deep hashing methods learn a whole set of hashing functions independently, while ignore the correlations between different hashing f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,858
2310.12393	Deep Learning Techniques for Video Instance Segmentation: A Survey	Video instance segmentation, also known as multi-object tracking and segmentation, is an emerging computer vision research area introduced in 2019, aiming at detecting, segmenting, and tracking instances in videos simultaneously. By tackling the video instance segmentation tasks through effective analysis and utilizati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	400,994
1312.6945	Quantum Ensemble Classification: A Sampling-based Learning Control Approach	Quantum ensemble classification has significant applications in discrimination of atoms (or molecules), separation of isotopic molecules and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles i...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	29,421
1107.4212	On the Undecidability of Fuzzy Description Logics with GCIs with Lukasiewicz t-norm	Recently there have been some unexpected results concerning Fuzzy Description Logics (FDLs) with General Concept Inclusions (GCIs). They show that, unlike the classical case, the DL ALC with GCIs does not have the finite model property under Lukasiewicz Logic or Product Logic and, specifically, knowledge base satisfiab...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	11,388
2010.04747	MEEP: An Open-Source Platform for Human-Human Dialog Collection and End-to-End Agent Training	We create a new task-oriented dialog platform (MEEP) where agents are given considerable freedom in terms of utterances and API calls, but are constrained to work within a push-button environment. We include facilities for collecting human-human dialog corpora, and for training automatic agents in an end-to-end fashion...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,848

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