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
2311.07470	Finding and Editing Multi-Modal Neurons in Pre-Trained Transformers	Understanding the internal mechanisms by which multi-modal large language models (LLMs) interpret different modalities and integrate cross-modal representations is becoming increasingly critical for continuous improvements in both academia and industry. In this paper, we propose a novel method to identify key neurons f...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	407,337
2106.11723	Neural Distributed Image Compression using Common Information	We present a novel deep neural network (DNN) architecture for compressing an image when a correlated image is available as side information only at the decoder. This problem is known as distributed source coding (DSC) in information theory. In particular, we consider a pair of stereo images, which generally have high c...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	242,484
2303.01268	Analyzing Effects of Fake Training Data on the Performance of Deep Learning Systems	Deep learning models frequently suffer from various problems such as class imbalance and lack of robustness to distribution shift. It is often difficult to find data suitable for training beyond the available benchmarks. This is especially the case for computer vision models. However, with the advent of Generative Adve...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	348,906
1206.4667	Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation	Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before ...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	16,718
1301.7375	Learning by Transduction	We describe a method for predicting a classification of an object given classifications of the objects in the training set, assuming that the pairs object/classification are generated by an i.i.d. process from a continuous probability distribution. Our method is a modification of Vapnik's support-vector machine; its ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	21,609
1209.4257	Communication-Efficient and Exact Clustering Distributed Streaming Data	A widely used approach to clustering a single data stream is the two-phased approach in which the online phase creates and maintains micro-clusters while the off-line phase generates the macro-clustering from the micro-clusters. We use this approach to propose a distributed framework for clustering streaming data. Our ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	18,632
2311.09522	Reversed Indexes $\approx$ Values in Wavelet Trees	This work presents a discovery to advance the wisdom in a particular Succinct Data Structure: Wavelet Tree (Grossi, Gupta, and Vitter 2003). The discovery is first made by showing the feasibility of Reversed Indexes = Values: for integers within $[0,2^{N})$, there exists a Wavelet Tree that its compressed indexes can b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	408,165
2411.07066	Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training	Network pruning focuses on computational techniques that aim to reduce a given model's computational cost by removing a subset of its parameters while having minimal impact on performance. Throughout the last decade, the most widely used pruning paradigm has been pruning and re-training, which nowadays is inconvenient ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	507,375
2303.17743	FairGen: Towards Fair Graph Generation	There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks, from gene regulatory networks to online transaction networks. Despite the remarkable success, the vast majority of these works are unsuperv...	false	false	false	true	false	false	true	false	false	false	false	true	false	false	false	false	false	false	355,324
2206.15083	UniDAformer: Unified Domain Adaptive Panoptic Segmentation Transformer via Hierarchical Mask Calibration	Domain adaptive panoptic segmentation aims to mitigate data annotation challenge by leveraging off-the-shelf annotated data in one or multiple related source domains. However, existing studies employ two separate networks for instance segmentation and semantic segmentation which lead to excessive network parameters as ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	305,481
2104.08481	Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes	We explore Few-Shot Learning (FSL) for Relation Classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories (none-of-the-above, aka NOTA), we first revisit the recent popular dataset structure for FSL, pointing out its unrealistic data distri...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	230,821
2411.17917	DECODE: Domain-aware Continual Domain Expansion for Motion Prediction	Motion prediction is critical for autonomous vehicles to effectively navigate complex environments and accurately anticipate the behaviors of other traffic participants. As autonomous driving continues to evolve, the need to assimilate new and varied driving scenarios necessitates frequent model updates through retrain...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	511,654
1712.05055	MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels	Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel technique of learning another neural network, called MentorNet, to supervise the training of the base deep networks, namely, StudentNet. During t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,686
1506.02080	Local Nonstationarity for Efficient Bayesian Optimization	Bayesian optimization has shown to be a fundamental global optimization algorithm in many applications: ranging from automatic machine learning, robotics, reinforcement learning, experimental design, simulations, etc. The most popular and effective Bayesian optimization relies on a surrogate model in the form of a Gaus...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	43,859
2402.17954	Twists, Humps, and Pebbles: Multilingual Speech Recognition Models Exhibit Gender Performance Gaps	Current automatic speech recognition (ASR) models are designed to be used across many languages and tasks without substantial changes. However, this broad language coverage hides performance gaps within languages, for example, across genders. Our study systematically evaluates the performance of two widely used multili...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	433,217
2111.03187	MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms	Missing data is an important problem in machine learning practice. Starting from the premise that imputation methods should preserve the causal structure of the data, we develop a regularization scheme that encourages any baseline imputation method to be causally consistent with the underlying data generating mechanism...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	265,077
1905.04479	DeepOPF: Deep Neural Network for DC Optimal Power Flow	We develop DeepOPF as a Deep Neural Network (DNN) approach for solving direct current optimal power flow (DC-OPF) problems. DeepOPF is inspired by the observation that solving DC-OPF for a given power network is equivalent to characterizing a high-dimensional mapping between the load inputs and the dispatch and transmi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	130,482
1208.0079	Probabilistic Databases with MarkoViews	Most of the work on query evaluation in probabilistic databases has focused on the simple tuple-independent data model, where tuples are independent random events. Several efficient query evaluation techniques exists in this setting, such as safe plans, algorithms based on OBDDs, tree-decomposition and a variety of app...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	17,856
2312.05549	Multi-granularity Causal Structure Learning	Unveil, model, and comprehend the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines. Meanwhile, new knowledge emerges when discovering causal relationships from data. Existing causal learning algorithms predominantly focus on the isolated effects of var...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	414,133
2204.03494	Deep Understanding based Multi-Document Machine Reading Comprehension	Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings. First, to understand the semantic meaning of words in the input question and documents from the perspective of each...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	290,317
2310.03657	Probabilistic Load Forecasting of Distribution Power Systems based on Empirical Copulas	Accurate and reliable electricity load forecasts are becoming increasingly important as the share of intermittent resources in the system increases. Distribution System Operators (DSOs) are called to accurately forecast their production and consumption to place optimal bids in the day-ahead market. Forecasts must accou...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	397,363
2103.15734	Enhanced Boundary Learning for Glass-like Object Segmentation	Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. However, this task is very challenging due to the arbitrary scenes behind glass-like objects. This paper aims to solve the glass-like object segm...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	227,324
1807.02021	A Semi-Analytical Method for Calculating Revisit Time for Satellite Constellations with Discontinuous Coverage	This paper presents a unique approach to the problem of calculating revisit time metrics for different satellite orbits, sensor geometries, and constellation configurations with application to early lifecycle design and optimisation processes for Earth observation missions. The developed semi-analytical approach uses a...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	102,184
0904.0226	Coding Versus ARQ in Fading Channels: How reliable should the PHY be?	This paper studies the tradeoff between channel coding and ARQ (automatic repeat request) in Rayleigh block-fading channels. A heavily coded system corresponds to a low transmission rate with few ARQ re-transmissions, whereas lighter coding corresponds to a higher transmitted rate but more re-transmissions. The optimum...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,462
2501.03957	Vision Language Models as Values Detectors	Large Language Models integrating textual and visual inputs have introduced new possibilities for interpreting complex data. Despite their remarkable ability to generate coherent and contextually relevant text based on visual stimuli, the alignment of these models with human perception in identifying relevant elements ...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	523,053
2106.06909	GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio	This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised and unsupervised training. Around 40,000 hours of transcribed audio is first collec...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	240,679
2307.09880	A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones	Accurate navigation is of paramount importance to ensure flight safety and efficiency for autonomous drones. Recent research starts to use Deep Neural Networks to enhance drone navigation given their remarkable predictive capability for visual perception. However, existing solutions either run DNN inference tasks on dr...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	380,333
2401.11374	Language Models as Hierarchy Encoders	Interpreting hierarchical structures latent in language is a key limitation of current language models (LMs). While previous research has implicitly leveraged these hierarchies to enhance LMs, approaches for their explicit encoding are yet to be explored. To address this, we introduce a novel approach to re-train trans...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	422,971
1904.12924	Agent-Based Simulations of Blockchain protocols illustrated via Kadena's Chainweb	While many distributed consensus protocols provide robust liveness and consistency guarantees under the presence of malicious actors, quantitative estimates of how economic incentives affect security are few and far between. In this paper, we describe a system for simulating how adversarial agents, both economically ra...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	true	129,248
1812.00651	Towards Agent-based Models of Rumours in Organizations: A Social Practice Theory Approach	Rumour is a collective emergent phenomenon with a potential for provoking a crisis. Modelling approaches have been deployed since five decades ago; however, the focus was mostly on epidemic behaviour of the rumours which does not take into account the differences of the agents. We use social practice theory to model ag...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	115,318
2309.04506	Unsupervised Gaze-aware Contrastive Learning with Subject-specific Condition	Appearance-based gaze estimation has shown great promise in many applications by using a single general-purpose camera as the input device. However, its success is highly depending on the availability of large-scale well-annotated gaze datasets, which are sparse and expensive to collect. To alleviate this challenge we ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	390,751
2302.08883	Approximately Bayes-Optimal Pseudo Label Selection	Semi-supervised learning by self-training heavily relies on pseudo-label selection (PLS). The selection often depends on the initial model fit on labeled data. Early overfitting might thus be propagated to the final model by selecting instances with overconfident but erroneous predictions, often referred to as confirma...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	346,216
2502.06650	Prototype Contrastive Consistency Learning for Semi-Supervised Medical Image Segmentation	Medical image segmentation is a crucial task in medical image analysis, but it can be very challenging especially when there are less labeled data but with large unlabeled data. Contrastive learning has proven to be effective for medical image segmentation in semi-supervised learning by constructing contrastive samples...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	532,168
2209.10100	Flashlight: Scalable Link Prediction with Effective Decoders	Link prediction (LP) has been recognized as an important task in graph learning with its broad practical applications. A typical application of LP is to retrieve the top scoring neighbors for a given source node, such as the friend recommendation. These services desire the high inference scalability to find the top sco...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	318,748
2008.04057	The Chess Transformer: Mastering Play using Generative Language Models	This work demonstrates that natural language transformers can support more generic strategic modeling, particularly for text-archived games. In addition to learning natural language skills, the abstract transformer architecture can generate meaningful moves on a chessboard. With further fine-tuning, the transformer lea...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	191,114
2407.07488	FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels	Federated learning is one popular paradigm to train a joint model in a distributed, privacy-preserving environment. But partial annotations pose an obstacle meaning that categories of labels are heterogeneous over clients. We propose to learn a joint backbone in a federated manner, while each site receives its own mult...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	471,782
2312.13307	Adaptive Training Meets Progressive Scaling: Elevating Efficiency in Diffusion Models	Diffusion models have demonstrated remarkable efficacy in various generative tasks with the predictive prowess of denoising model. Currently, diffusion models employ a uniform denoising model across all timesteps. However, the inherent variations in data distributions at different timesteps lead to conflicts during tra...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	417,268
2303.10585	Label Name is Mantra: Unifying Point Cloud Segmentation across Heterogeneous Datasets	Point cloud segmentation is a fundamental task in 3D vision that serves a wide range of applications. Although great progresses have been made these years, its practical usability is still limited by the availability of training data. Existing approaches cannot make full use of multiple datasets on hand due to the labe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,510
2308.16742	Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains	During the process of computed tomography (CT), metallic implants often cause disruptive artifacts in the reconstructed images, impeding accurate diagnosis. Several supervised deep learning-based approaches have been proposed for reducing metal artifacts (MAR). However, these methods heavily rely on training with simul...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	389,096
2007.12107	Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild	Detecting objects and estimating their viewpoints in images are key tasks of 3D scene understanding. Recent approaches have achieved excellent results on very large benchmarks for object detection and viewpoint estimation. However, performances are still lagging behind for novel object categories with few samples. In t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,734
2006.06823	Combining the band-limited parameterization and Semi-Lagrangian Runge--Kutta integration for efficient PDE-constrained LDDMM	The family of PDE-constrained LDDMM methods is emerging as a particularly interesting approach for physically meaningful diffeomorphic transformations. The original combination of Gauss--Newton--Krylov optimization and Runge--Kutta integration, shows excellent numerical accuracy and fast convergence rate. However, its ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	181,565
2001.00360	Kernelized Support Tensor Train Machines	Tensor, a multi-dimensional data structure, has been exploited recently in the machine learning community. Traditional machine learning approaches are vector- or matrix-based, and cannot handle tensorial data directly. In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	159,199
2502.03907	No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking	We analyze the capabilities of foundation models addressing the tedious task of generating annotations for animal tracking. Annotating a large amount of data is vital and can be a make-or-break factor for the robustness of a tracking model. Robustness is particularly crucial in animal tracking, as accurate tracking ove...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	530,907
0903.1765	A Lower Bound on Arbitrary $f$--Divergences in Terms of the Total Variation	An important tool to quantify the likeness of two probability measures are f-divergences, which have seen widespread application in statistics and information theory. An example is the total variation, which plays an exceptional role among the f-divergences. It is shown that every f-divergence is bounded from below by ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,323
2312.02017	A multi-channel cycleGAN for CBCT to CT synthesis	Image synthesis is used to generate synthetic CTs (sCTs) from on-treatment cone-beam CTs (CBCTs) with a view to improving image quality and enabling accurate dose computation to facilitate a CBCT-based adaptive radiotherapy workflow. As this area of research gains momentum, developments in sCT generation methods are di...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,662
2309.16940	Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow	Collaborative perception can substantially boost each agent's perception ability by facilitating communication among multiple agents. However, temporal asynchrony among agents is inevitable in the real world due to communication delays, interruptions, and clock misalignments. This issue causes information mismatch duri...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	395,562
2405.16277	Picturing Ambiguity: A Visual Twist on the Winograd Schema Challenge	Large Language Models (LLMs) have demonstrated remarkable success in tasks like the Winograd Schema Challenge (WSC), showcasing advanced textual common-sense reasoning. However, applying this reasoning to multimodal domains, where understanding text and images together is essential, remains a substantial challenge. To ...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	457,342
2104.03886	Classification, Slippage, Failure and Discovery	This text argues for the potential of machine learning infused classification systems as vectors for a technically-engaged and constructive technology critique. The text describes this potential with several experiments in image data creation and neural network based classification. The text considers varying aspects o...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	229,210
2208.02432	Image-based Contextual Pill Recognition with Medical Knowledge Graph Assistance	Identifying pills given their captured images under various conditions and backgrounds has been becoming more and more essential. Several efforts have been devoted to utilizing the deep learning-based approach to tackle the pill recognition problem in the literature. However, due to the high similarity between pills' a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	311,459
2306.11626	Soft Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity	Robust Markov Decision Processes (MDPs) and risk-sensitive MDPs are both powerful tools for making decisions in the presence of uncertainties. Previous efforts have aimed to establish their connections, revealing equivalences in specific formulations. This paper introduces a new formulation for risk-sensitive MDPs, whi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,658
1701.03572	Real-Time Optical flow-based Video Stabilization for Unmanned Aerial Vehicles	This paper describes the development of a novel algorithm to tackle the problem of real-time video stabilization for unmanned aerial vehicles (UAVs). There are two main components in the algorithm: (1) By designing a suitable model for the global motion of UAV, the proposed algorithm avoids the necessity of estimating ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	66,723
2007.04604	Building an Automated Gesture Imitation Game for Teenagers with ASD	Autism spectrum disorder is a neurodevelopmental condition that includes issues with communication and social interactions. People with ASD also often have restricted interests and repetitive behaviors. In this paper we build preliminary bricks of an automated gesture imitation game that will aim at improving social in...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,410
2004.10024	Example-Guided Image Synthesis across Arbitrary Scenes using Masked Spatial-Channel Attention and Self-Supervision	Example-guided image synthesis has recently been attempted to synthesize an image from a semantic label map and an exemplary image. In the task, the additional exemplar image provides the style guidance that controls the appearance of the synthesized output. Despite the controllability advantage, the existing models ar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,520
2105.07799	Efficient yield optimization with limited gradient information	In this work an efficient strategy for yield optimization with uncertain and deterministic optimization variables is presented. The gradient based adaptive Newton-Monte Carlo method is modified, such that it can handle variables with (uncertain parameters) and without (deterministic parameters) analytical gradient info...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	235,561
2410.17410	Learning Graph Filters for Structure-Function Coupling based Hub Node Identification	Over the past two decades, tools from network science have been leveraged to characterize the organization of both structural and functional networks of the brain. One such measure of network organization is hub node identification. Hubs are specialized nodes within a network that link distinct brain units correspondin...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	501,446
2104.02525	Searching Efficient Model-guided Deep Network for Image Denoising	Neural architecture search (NAS) has recently reshaped our understanding on various vision tasks. Similar to the success of NAS in high-level vision tasks, it is possible to find a memory and computationally efficient solution via NAS with highly competent denoising performance. However, the optimization gap between th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	228,746
2006.02434	Visual Summarization of Lecture Video Segments for Enhanced Navigation	Lecture videos are an increasingly important learning resource for higher education. However, the challenge of quickly finding the content of interest in a lecture video is an important limitation of this format. This paper introduces visual summarization of lecture video segments to enhance navigation. A lecture video...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	180,039
2010.13270	Improved Mask-CTC for Non-Autoregressive End-to-End ASR	For real-world deployment of automatic speech recognition (ASR), the system is desired to be capable of fast inference while relieving the requirement of computational resources. The recently proposed end-to-end ASR system based on mask-predict with connectionist temporal classification (CTC), Mask-CTC, fulfills this d...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	203,061
2112.06580	How to Find a Good Explanation for Clustering?	$k$-means and $k$-median clustering are powerful unsupervised machine learning techniques. However, due to complicated dependences on all the features, it is challenging to interpret the resulting cluster assignments. Moshkovitz, Dasgupta, Rashtchian, and Frost [ICML 2020] proposed an elegant model of explainable $k$-m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	271,230
1912.00728	Joint Active and Passive Beamforming for Intelligent Reflecting Surface-Assisted Massive MIMO Systems	In this paper, we study the problem of joint active and passive beamforming for intelligent reflecting surface (IRS)-assisted massive MIMO systems, where multiple IRSs equipped with a large number of passive elements are deployed to assist a base station (BS) to simultaneously serve a small number of single-antenna use...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	155,867
2305.16585	ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation	Paraphrase generation is a long-standing task in natural language processing (NLP). Supervised paraphrase generation models, which rely on human-annotated paraphrase pairs, are cost-inefficient and hard to scale up. On the other hand, automatically annotated paraphrase pairs (e.g., by machine back-translation), usually...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	368,162
2402.14424	Automating psychological hypothesis generation with AI: when large language models meet causal graph	Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal ...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	431,681
2305.04518	Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT	This study investigates the feasibility of developing an Artificial General Recommender (AGR), facilitated by recent advancements in Large Language Models (LLMs). An AGR comprises both conversationality and universality to engage in natural dialogues and generate recommendations across various domains. We propose ten f...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	362,802
2301.12466	Kernelized Cumulants: Beyond Kernel Mean Embeddings	In $\mathbb R^d$, it is well-known that cumulants provide an alternative to moments that can achieve the same goals with numerous benefits such as lower variance estimators. In this paper we extend cumulants to reproducing kernel Hilbert spaces (RKHS) using tools from tensor algebras and show that they are computationa...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	342,539
2308.11224	Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis	Large Language Models (LLMs) have garnered considerable interest within both academic and industrial. Yet, the application of LLMs to graph data remains under-explored. In this study, we evaluate the capabilities of four LLMs in addressing several analytical problems with graph data. We employ four distinct evaluation ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	387,055
2403.01622	A Human-Centered Approach for Bootstrapping Causal Graph Creation	Causal inference, a cornerstone in disciplines such as economics, genomics, and medicine, is increasingly being recognized as fundamental to advancing the field of robotics. In particular, the ability to reason about cause and effect from observational data is crucial for robust generalization in robotic systems. Howev...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	434,505
2006.09438	Off-policy Bandits with Deficient Support	Learning effective contextual-bandit policies from past actions of a deployed system is highly desirable in many settings (e.g. voice assistants, recommendation, search), since it enables the reuse of large amounts of log data. State-of-the-art methods for such off-policy learning, however, are based on inverse propens...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	182,538
2308.08775	Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation	We aim at incorporating explicit shape information into current 3D organ segmentation models. Different from previous works, we formulate shape learning as an in-painting task, which is named Masked Label Mask Modeling (MLM). Through MLM, learnable mask tokens are fed into transformer blocks to complete the label mask ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,028
2502.04021	Variational Quantum Optimization with Continuous Bandits	We introduce a novel approach to variational Quantum algorithms (VQA) via continuous bandits. VQA are a class of hybrid Quantum-classical algorithms where the parameters of Quantum circuits are optimized by classical algorithms. Previous work has used zero and first order gradient based methods, however such algorithms...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,952
2403.05518	Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought	While chain-of-thought prompting (CoT) has the potential to improve the explainability of language model reasoning, it can systematically misrepresent the factors influencing models' behavior--for example, rationalizing answers in line with a user's opinion without mentioning this bias. To mitigate this biased reasonin...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	436,032
1501.05405	Proceedings 4th Workshop on Hybrid Autonomous Systems	The interest in autonomous systems is increasing both in industry and academia. Such systems must operate with limited human intervention in a changing environment and must be able to compensate for significant system failures without external intervention. The most appropriate models of autonomous systems can be found...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	true	39,476
2007.07218	Learning Accurate and Human-Like Driving using Semantic Maps and Attention	This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. To tackle the first issue we exploit semantic and visual maps from HERE Technologies and augment the existing Drive360 dataset with such. The maps are used in an attention mechanism that promotes segmentation ...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	187,266
2008.06218	Which Strategies Matter for Noisy Label Classification? Insight into Loss and Uncertainty	Label noise is a critical factor that degrades the generalization performance of deep neural networks, thus leading to severe issues in real-world problems. Existing studies have employed strategies based on either loss or uncertainty to address noisy labels, and ironically some strategies contradict each other: emphas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	191,734
2311.06798	MetaMix: Meta-state Precision Searcher for Mixed-precision Activation Quantization	Mixed-precision quantization of efficient networks often suffer from activation instability encountered in the exploration of bit selections. To address this problem, we propose a novel method called MetaMix which consists of bit selection and weight training phases. The bit selection phase iterates two steps, (1) the ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	407,083
2404.16900	Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging	This paper focuses on the development of a space-variant regularization model for solving an under-determined linear inverse problem. The case study is a medical image reconstruction from few-view tomographic noisy data. The primary objective of the proposed optimization model is to achieve a good balance between denoi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	449,668
2105.08052	The Boombox: Visual Reconstruction from Acoustic Vibrations	Interacting with bins and containers is a fundamental task in robotics, making state estimation of the objects inside the bin critical. While robots often use cameras for state estimation, the visual modality is not always ideal due to occlusions and poor illumination. We introduce The Boombox, a container that uses so...	false	false	true	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	235,639
2410.19444	Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment	Automatically recognizing emotional intent using facial expression has been a thoroughly investigated topic in the realm of computer vision. Facial Expression Recognition (FER), being a supervised learning task, relies heavily on substantially large data exemplifying various socio-cultural demographic attributes. Over ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	502,313
2311.03071	OrthoNets: Orthogonal Channel Attention Networks	Designing an effective channel attention mechanism implores one to find a lossy-compression method allowing for optimal feature representation. Despite recent progress in the area, it remains an open problem. FcaNet, the current state-of-the-art channel attention mechanism, attempted to find such an information-rich co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	405,702
2406.01922	Performance Analysis of Hybrid Cellular and Cell-free MIMO Network	Cell-free wireless communication is envisioned as one of the most promising network architectures, which can achieve stable and uniform communication performance while improving the system energy and spectrum efficiency. The deployment of cell-free networks is envisioned to be a longterm evolutionary process, in which ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	460,526
2211.07556	Utilizing Synthetic Data in Supervised Learning for Robust 5-DoF Magnetic Marker Localization	Tracking passive magnetic markers plays a vital role in advancing healthcare and robotics, offering the potential to significantly improve the precision and efficiency of systems. This technology is key to developing smarter, more responsive tools and devices, such as enhanced surgical instruments, precise diagnostic t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	330,286
1704.01053	Network-ensemble comparisons with stochastic rewiring and von Neumann entropy	Assessing whether a given network is typical or atypical for a random-network ensemble (i.e., network-ensemble comparison) has widespread applications ranging from null-model selection and hypothesis testing to clustering and classifying networks. We develop a framework for network-ensemble comparison by subjecting the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	71,186
1309.4156	Trade integration and trade imbalances in the European Union: a network perspective	We study the ever more integrated and ever more unbalanced trade relationships between European countries. To better capture the complexity of economic networks, we propose two global measures that assess the trade integration and the trade imbalances of the European countries. These measures are the network (or indire...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	27,086
2406.18849	Dysca: A Dynamic and Scalable Benchmark for Evaluating Perception Ability of LVLMs	Currently many benchmarks have been proposed to evaluate the perception ability of the Large Vision-Language Models (LVLMs). However, most benchmarks conduct questions by selecting images from existing datasets, resulting in the potential data leakage. Besides, these benchmarks merely focus on evaluating LVLMs on the r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	468,199
2110.14982	A Quasi-Optimal Factorization Preconditioner for Periodic Schr\"odinger Eigenstates in Anisotropically Expanding Domains	This paper provides a provably quasi-optimal preconditioning strategy of the linear Schr\"odinger eigenvalue problem with periodic potentials for a possibly non-uniform spatial expansion of the domain. The quasi-optimality is achieved by having the iterative eigenvalue algorithms converge in a constant number of iterat...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	263,718
1912.02470	Blind Inpainting of Large-scale Masks of Thin Structures with Adversarial and Reinforcement Learning	Several imaging applications (vessels, retina, plant roots, road networks from satellites) require the accurate segmentation of thin structures for subsequent analysis. Discontinuities (gaps) in the extracted foreground may hinder down-stream image-based analysis of biomarkers, organ structure and topology. In this pap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	156,357
2404.13820	Prove Symbolic Regression is NP-hard by Symbol Graph	Symbolic regression (SR) is the task of discovering a symbolic expression that fits a given data set from the space of mathematical expressions. Despite the abundance of research surrounding the SR problem, there's a scarcity of works that confirm its NP-hard nature. Therefore, this paper introduces the concept of a sy...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	448,455
2007.01413	Wearable Respiration Monitoring: Interpretable Inference with Context and Sensor Biomarkers	Breathing rate (BR), minute ventilation (VE), and other respiratory parameters are essential for real-time patient monitoring in many acute health conditions, such as asthma. The clinical standard for measuring respiration, namely Spirometry, is hardly suitable for continuous use. Wearables can track many physiological...	true	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	185,417
1904.06501	Maximum Correntropy Criterion with Variable Center	Correntropy is a local similarity measure defined in kernel space and the maximum correntropy criterion (MCC) has been successfully applied in many areas of signal processing and machine learning in recent years. The kernel function in correntropy is usually restricted to the Gaussian function with center located at ze...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	127,567
2303.16892	Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation	Transformers have shown great success in medical image segmentation. However, transformers may exhibit a limited generalization ability due to the underlying single-scale self-attention (SA) mechanism. In this paper, we address this issue by introducing a Multi-scale hiERarchical vIsion Transformer (MERIT) backbone net...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	355,030
2409.01154	Forecasting infectious disease prevalence with associated uncertainty using neural networks	Infectious diseases pose significant human and economic burdens. Accurately forecasting disease incidence can enable public health agencies to respond effectively to existing or emerging diseases. Despite progress in the field, developing accurate forecasting models remains a significant challenge. This thesis proposes...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	485,234
2103.11517	Dual Monte Carlo Tree Search	AlphaZero, using a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS), has successfully trained reinforcement learning agents in a tabula-rasa way. The neural MCTS algorithm has been successful in finding near-optimal strategies for games through self-play. However, the AlphaZero algorithm has a sig...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	225,830
2404.02581	Multi-Granularity Guided Fusion-in-Decoder	In Open-domain Question Answering (ODQA), it is essential to discern relevant contexts as evidence and avoid spurious ones among retrieved results. The model architecture that uses concatenated multiple contexts in the decoding phase, i.e., Fusion-in-Decoder, demonstrates promising performance but generates incorrect o...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	443,923
2406.02285	Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR Models	Recent advancements in Self-Supervised Learning (SSL) have shown promising results in Speaker Verification (SV). However, narrowing the performance gap with supervised systems remains an ongoing challenge. Several studies have observed that speech representations from large-scale ASR models contain valuable speaker inf...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	460,699
2403.09389	Learning to optimize with convergence guarantees using nonlinear system theory	The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient descent methods offer strong theoretical guarantees for convex proble...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	437,745
2304.05153	Regression-based Deep-Learning predicts molecular biomarkers from pathology slides	Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Th...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	357,513
2306.10458	Weakly Supervised Regression with Interval Targets	This paper investigates an interesting weakly supervised regression setting called regression with interval targets (RIT). Although some of the previous methods on relevant regression settings can be adapted to RIT, they are not statistically consistent, and thus their empirical performance is not guaranteed. In this p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,236
2308.05441	Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation	We propose an experimental method for measuring bias in face recognition systems. Existing methods to measure bias depend on benchmark datasets that are collected in the wild and annotated for protected (e.g., race, gender) and non-protected (e.g., pose, lighting) attributes. Such observational datasets only permit cor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,791
2112.00616	Roadmap for Edge AI: A Dagstuhl Perspective	Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and all...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	269,193
2501.17782	Picard-KKT-hPINN: Enforcing Nonlinear Enthalpy Balances for Physically Consistent Neural Networks	Neural networks are widely used as surrogate models but they do not guarantee physically consistent predictions thereby preventing adoption in various applications. We propose a method that can enforce NNs to satisfy physical laws that are nonlinear in nature such as enthalpy balances. Our approach, inspired by Picard ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	528,453
2203.16767	SpatioTemporal Focus for Skeleton-based Action Recognition	Graph convolutional networks (GCNs) are widely adopted in skeleton-based action recognition due to their powerful ability to model data topology. We argue that the performance of recent proposed skeleton-based action recognition methods is limited by the following factors. First, the predefined graph structures are sha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	288,908

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