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
2205.00633	Robust Fine-tuning via Perturbation and Interpolation from In-batch Instances	Fine-tuning pretrained language models (PLMs) on downstream tasks has become common practice in natural language processing. However, most of the PLMs are vulnerable, e.g., they are brittle under adversarial attacks or imbalanced data, which hinders the application of the PLMs on some downstream tasks, especially in sa...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	294,329
2309.16744	Predicting Long-term Renal Impairment in Post-COVID-19 Patients with Machine Learning Algorithms	The COVID-19 pandemic has had far-reaching implications for global public health. As we continue to grapple with its consequences, it becomes increasingly clear that post-COVID-19 complications are a significant concern. Among these complications, renal impairment has garnered particular attention due to its potential ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	395,484
1404.3525	Distributed Asynchronous Optimization Framework for the MISO Interference Channel	We study the distributed optimization of transmit strategies in a multiple-input, single-output (MISO) interference channel (IFC). Existing distributed algorithms rely on stricly synchronized update steps by the individual users. They require a global synchronization mechanism and potentially suffer from the synchroniz...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,318
2303.04178	SALSA PICANTE: a machine learning attack on LWE with binary secrets	Learning with Errors (LWE) is a hard math problem underpinning many proposed post-quantum cryptographic (PQC) systems. The only PQC Key Exchange Mechanism (KEM) standardized by NIST is based on module~LWE, and current publicly available PQ Homomorphic Encryption (HE) libraries are based on ring LWE. The security of LWE...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	349,981
2408.10012	CLIPCleaner: Cleaning Noisy Labels with CLIP	Learning with Noisy labels (LNL) poses a significant challenge for the Machine Learning community. Some of the most widely used approaches that select as clean samples for which the model itself (the in-training model) has high confidence, e.g., `small loss', can suffer from the so called `self-confirmation' bias. This...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	481,685
1609.05502	Inverse Problems with Invariant Multiscale Statistics	We propose a new approach to linear ill-posed inverse problems. Our algorithm alternates between enforcing two constraints: the measurements and the statistical correlation structure in some transformed space. We use a non-linear multiscale scattering transform which discards the phase and thus exposes strong spectral ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	61,149
2311.11073	Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training	In recent years, graph contrastive learning (GCL) has emerged as one of the optimal solutions for various supervised tasks at the node level. However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	408,782
2207.06680	Equivariant Hypergraph Diffusion Neural Operators	Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide a promising way to model higher-order relations in data and further solve relevant prediction tasks built upon such higher-order relations. However, higher-order relations in practice contain complex patterns and are often highly irre...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	307,961
2104.09351	The Impact of COVID-19 on Urban Energy Consumption of the Commercial Tourism City	In 2020, the COVID-19 pandemic spreads all over the world. In order to alleviate the spread of the epidemic, various blockade policies have been implemented in many areas. In order to formulate a better epidemic prevention policy for urban energy consumption of the commercial tourism cities, this paper first analyses t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	231,205
1906.05419	Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation	In this work we aim to obtain computationally-efficient uncertainty estimates with deep networks. For this, we propose a modified knowledge distillation procedure that achieves state-of-the-art uncertainty estimates both for in and out-of-distribution samples. Our contributions include a) demonstrating and adapting to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	135,019
2109.15119	Improved statistical machine translation using monolingual paraphrases	We propose a novel monolingual sentence paraphrasing method for augmenting the training data for statistical machine translation systems "for free" -- by creating it from data that is already available rather than having to create more aligned data. Starting with a syntactic tree, we recursively generate new sentence v...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	258,182
2307.14603	A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors	The presence of tertiary lymphoid structures (TLSs) on pancreatic pathological images is an important prognostic indicator of pancreatic tumors. Therefore, TLSs detection on pancreatic pathological images plays a crucial role in diagnosis and treatment for patients with pancreatic tumors. However, fully supervised dete...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	381,987
1610.02918	Phase transitions and optimal algorithms in high-dimensional Gaussian mixture clustering	We consider the problem of Gaussian mixture clustering in the high-dimensional limit where the data consists of $m$ points in $n$ dimensions, $n,m \rightarrow \infty$ and $\alpha = m/n$ stays finite. Using exact but non-rigorous methods from statistical physics, we determine the critical value of $\alpha$ and the dista...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	62,179
2403.10582	How Suboptimal is Training rPPG Models with Videos and Targets from Different Body Sites?	Remote camera measurement of the blood volume pulse via photoplethysmography (rPPG) is a compelling technology for scalable, low-cost, and accessible assessment of cardiovascular information. Neural networks currently provide the state-of-the-art for this task and supervised training or fine-tuning is an important step...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	438,261
2205.07178	Optimal Congestion-aware Routing and Offloading in Collaborative Edge Computing	Collaborative edge computing (CEC) is an emerging paradigm where heterogeneous edge devices collaborate to fulfill computation tasks, such as model training or video processing, by sharing communication and computation resources. Nevertheless, the optimal data/result routing and computation offloading strategy in CEC w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	296,505
2204.03929	Deep Hyperspectral-Depth Reconstruction Using Single Color-Dot Projection	Depth reconstruction and hyperspectral reflectance reconstruction are two active research topics in computer vision and image processing. Conventionally, these two topics have been studied separately using independent imaging setups and there is no existing method which can acquire depth and spectral reflectance simult...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,480
1901.05127	Attention-aware Multi-stroke Style Transfer	Neural style transfer has drawn considerable attention from both academic and industrial field. Although visual effect and efficiency have been significantly improved, existing methods are unable to coordinate spatial distribution of visual attention between the content image and stylized image, or render diverse level...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,733
2004.01141	Predictive Bandits	We introduce and study a new class of stochastic bandit problems, referred to as predictive bandits. In each round, the decision maker first decides whether to gather information about the rewards of particular arms (so that their rewards in this round can be predicted). These measurements are costly, and may be corrup...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,825
1105.4737	Sufficient Stochastic Maximum Principle for Discounted Control Problem	In this article, the sufficient Pontryagin's maximum principle for infinite horizon discounted stochastic control problem is established. The sufficiency is ensured by an additional assumption of concavity of the Hamiltonian function. Throughout the paper, it is assumed that the control domain U is a convex set and the...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	10,482
2307.16143	Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation	Medical image synthesis is a challenging task due to the scarcity of paired data. Several methods have applied CycleGAN to leverage unpaired data, but they often generate inaccurate mappings that shift the anatomy. This problem is further exacerbated when the images from the source and target modalities are heavily mis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	382,490
2402.06521	Reconstructing facade details using MLS point clouds and Bag-of-Words approach	In the reconstruction of fa\c{c}ade elements, the identification of specific object types remains challenging and is often circumvented by rectangularity assumptions or the use of bounding boxes. We propose a new approach for the reconstruction of 3D fa\c{c}ade details. We combine MLS point clouds and a pre-defined 3D ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	428,326
2011.14440	Temporal assortment of cooperators in the spatial prisoner's dilemma	We study a spatial, one-shot prisoner's dilemma (PD) model in which selection operates on both an organism's behavioral strategy (cooperate or defect) and its choice of when to implement that strategy across a set of discrete time slots. Cooperators evolve to fixation regularly in the model when we add time slots to la...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	208,777
2411.04472	Accurate Calculation of Switching Events in Electromagnetic Transient Simulation Considering State Variable Discontinuities	Accurate calculation of switching events is important for electromagnetic transient simulation to obtain reliable results. The common presumption of continuous differential state variables could prevent the accurate calculation, thus leading to unreliable results. This paper explores accurately calculating switching ev...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	506,285
2402.06827	RAMP: Boosting Adversarial Robustness Against Multiple $l_p$ Perturbations for Universal Robustness	Most existing works focus on improving robustness against adversarial attacks bounded by a single $l_p$ norm using adversarial training (AT). However, these AT models' multiple-norm robustness (union accuracy) is still low, which is crucial since in the real-world an adversary is not necessarily bounded by a single nor...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	428,454
1905.05279	Deep Local Trajectory Replanning and Control for Robot Navigation	We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a goal, and a deep local trajectory planner and velocity controller to compute motion commands. The latter components of the system adjust the...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	130,673
0909.2336	Two-Phase Flow in Heterogeneous Media	In this study, we investigate the appeared complexity of two-phase flow (air-water) in a heterogeneous soil where the supposed porous media is non-deformable media which is under the time-dependent gas pressure. After obtaining of governing equations and considering the capillary pressure-saturation and permeability fu...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	4,480
1801.09334	Ensemble Neural Relation Extraction with Adaptive Boosting	Relation extraction has been widely studied to extract new relational facts from open corpus. Previous relation extraction methods are faced with the problem of wrong labels and noisy data, which substantially decrease the performance of the model. In this paper, we propose an ensemble neural network model - Adaptive B...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	89,086
2407.06116	Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology	Understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions. H&E is widely available, however, cell subtyping often requires expert knowledge and the use of specialized stains. To reduce the annotation burden, AI has been proposed for the cl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	471,262
2202.03704	Budgeted Combinatorial Multi-Armed Bandits	We consider a budgeted combinatorial multi-armed bandit setting where, in every round, the algorithm selects a super-arm consisting of one or more arms. The goal is to minimize the total expected regret after all rounds within a limited budget. Existing techniques in this literature either fix the budget per round or f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	279,313
1812.07671	Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL	Humans and animals can learn complex predictive models that allow them to accurately and reliably reason about real-world phenomena, and they can adapt such models extremely quickly in the face of unexpected changes. Deep neural network models allow us to represent very complex functions, but lack this capacity for rap...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	116,857
2405.19877	KNOW: A Real-World Ontology for Knowledge Capture with Large Language Models	We present KNOW--the Knowledge Navigator Ontology for the World--the first ontology designed to capture everyday knowledge to augment large language models (LLMs) in real-world generative AI use cases such as personal AI assistants. Our domain is human life, both its everyday concerns and its major milestones. We have ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	459,104
2406.05802	SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attention	In the domain of large foundation models, the Segment Anything Model (SAM) has gained notable recognition for its exceptional performance in image segmentation. However, tackling the video camouflage object detection (VCOD) task presents a unique challenge. Camouflaged objects typically blend into the background, makin...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	462,298
2302.05313	Discovery of sparse hysteresis models for piezoelectric materials	This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression techniques. While sparse regression has previously been used to model various scientific and engineering phenomena, its application to nonlinea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	345,001
2410.21028	Graph Based Traffic Analysis and Delay Prediction	This research is focused on traffic congestion in the small island of Malta which is the most densely populated country in the EU with about 1,672 inhabitants per square kilometre (4,331 inhabitants/sq mi). Furthermore, Malta has a rapid vehicle growth. Based on our research, the number of vehicles increased by around ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	503,059
2502.05286	Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration and Characterization	We introduce an enumeration-free method based on mathematical programming to precisely characterize various properties such as fairness or sparsity within the set of "good models", known as Rashomon set. This approach is generically applicable to any hypothesis class, provided that a mathematical formulation of the mod...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	531,535
2311.12884	Identifying DNA Sequence Motifs Using Deep Learning	Splice sites play a crucial role in gene expression, and accurate prediction of these sites in DNA sequences is essential for diagnosing and treating genetic disorders. We address the challenge of splice site prediction by introducing DeepDeCode, an attention-based deep learning sequence model to capture the long-term ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	409,537
1406.1022	Navigating in a sea of repeats in RNA-seq without drowning	The main challenge in de novo assembly of NGS data is certainly to deal with repeats that are longer than the reads. This is particularly true for RNA- seq data, since coverage information cannot be used to flag repeated sequences, of which transposable elements are one of the main examples. Most transcriptome assemble...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	33,590
2310.03224	Matrix Completion from One-Bit Dither Samples	We explore the impact of coarse quantization on matrix completion in the extreme scenario of dithered one-bit sensing, where the matrix entries are compared with time-varying threshold levels. In particular, instead of observing a subset of high-resolution entries of a low-rank matrix, we have access to a small number ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	397,189
2307.12307	Robust Weighted Sum-Rate Maximization for Transmissive RIS Transmitter Enabled RSMA Networks	Due to the low power consumption and low cost nature of transmissive reconfigurable intelligent surface (RIS),in this paper, we propose a downlink multi-user rate-splitting multiple access (RSMA) architecture based on the transmissive RIS transmitter, where the channel state information (CSI) is only accquired partiall...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	381,220
1405.0894	Interactive Function Computation via Polar Coding	In a series of papers N. Ma and P. Ishwar (2011-13) considered a range of distributed source coding problems that arise in the context of iterative computation of functions, characterizing the region of achievable communication rates. We consider the problems of interactive computation of functions by two terminals and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,820
2210.00645	Economic-Driven Adaptive Traffic Signal Control	With the emerging connected-vehicle technologies and smart roads, the need for intelligent adaptive traffic signal controls is more than ever before. This paper proposes a novel Economic-driven Adaptive Traffic Signal Control (eATSC) model with a hyper control variable - interest rate defined in economics for traffic s...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	320,945
2310.05055	FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis	Training models with robust group fairness properties is crucial in ethically sensitive application areas such as medical diagnosis. Despite the growing body of work aiming to minimise demographic bias in AI, this problem remains challenging. A key reason for this challenge is the fairness generalisation gap: High-capa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	397,957
2008.13196	Finding Action Tubes with a Sparse-to-Dense Framework	The task of spatial-temporal action detection has attracted increasing attention among researchers. Existing dominant methods solve this problem by relying on short-term information and dense serial-wise detection on each individual frames or clips. Despite their effectiveness, these methods showed inadequate use of lo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,788
2204.07965	Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint	Active learning is a promising alternative to alleviate the issue of high annotation cost in the computer vision tasks by consciously selecting more informative samples to label. Active learning for object detection is more challenging and existing efforts on it are relatively rare. In this paper, we propose a novel hy...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	291,918
2209.05649	Social-PatteRNN: Socially-Aware Trajectory Prediction Guided by Motion Patterns	As robots across domains start collaborating with humans in shared environments, algorithms that enable them to reason over human intent are important to achieve safe interplay. In our work, we study human intent through the problem of predicting trajectories in dynamic environments. We explore domains where navigation...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	317,154
2407.08700	Flex-TPU: A Flexible TPU with Runtime Reconfigurable Dataflow Architecture	Tensor processing units (TPUs) are one of the most well-known machine learning (ML) accelerators utilized at large scale in data centers as well as in tiny ML applications. TPUs offer several improvements and advantages over conventional ML accelerators, like graphical processing units (GPUs), being designed specifical...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	472,271
1704.04720	Understanding Norm Change: An Evolutionary Game-Theoretic Approach (Extended Version)	Human societies around the world interact with each other by developing and maintaining social norms, and it is critically important to understand how such norms emerge and change. In this work, we define an evolutionary game-theoretic model to study how norms change in a society, based on the idea that different stren...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	false	false	true	71,874
2411.11603	Feature Selection for Network Intrusion Detection	Network Intrusion Detection (NID) remains a key area of research within the information security community, while also being relevant to Machine Learning (ML) practitioners. The latter generally aim to detect attacks using network features, which have been extracted from raw network data typically using dimensionality ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	509,113
2405.01122	Generative Relevance Feedback and Convergence of Adaptive Re-Ranking: University of Glasgow Terrier Team at TREC DL 2023	This paper describes our participation in the TREC 2023 Deep Learning Track. We submitted runs that apply generative relevance feedback from a large language model in both a zero-shot and pseudo-relevance feedback setting over two sparse retrieval approaches, namely BM25 and SPLADE. We couple this first stage with adap...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	451,235
2108.07403	FARF: A Fair and Adaptive Random Forests Classifier	As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning algorithms focus on the offline setting. However, in many real-world applications data comes in an online fashion and needs to be processed ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	250,904
1804.01489	On the internal signature and minimal electric network realizations of reciprocal behaviors	In a recent paper, it was shown that (i) any reciprocal system with a proper transfer function possesses a signature-symmetric realization in which each state has either even or odd parity; and (ii) any reciprocal and passive behavior can be realized as the driving-point behavior of an electric network comprising resis...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	94,231
1707.00907	The Candidate Multi-Cut for Cell Segmentation	Two successful approaches for the segmentation of biomedical images are (1) the selection of segment candidates from a merge-tree, and (2) the clustering of small superpixels by solving a Multi-Cut problem. In this paper, we introduce a model that unifies both approaches. Our model, the Candidate Multi-Cut (CMC), allow...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	76,441
2106.03349	A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It	In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, the researchers have mainly focused on the coding procedure that outputs the coded sequence from the input image, and the assumption of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	239,281
1812.01481	On the stability of nucleic acid feedback controllers	Recent work has shown how chemical reaction network theory may be used to design dynamical systems that can be implemented biologically in nucleic acid-based chemistry. While this has allowed the construction of advanced open-loop circuitry based on cascaded DNA strand displacement (DSD) reactions, little progress has ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	115,527
2305.18456	Baselines for Identifying Watermarked Large Language Models	We consider the emerging problem of identifying the presence and use of watermarking schemes in widely used, publicly hosted, closed source large language models (LLMs). We introduce a suite of baseline algorithms for identifying watermarks in LLMs that rely on analyzing distributions of output tokens and logits genera...	false	false	false	false	true	false	true	false	false	false	false	false	true	true	false	false	false	false	369,072
1412.6791	Mixture of Parts Revisited: Expressive Part Interactions for Pose Estimation	Part-based models with restrictive tree-structured interactions for the Human Pose Estimation problem, leaves many part interactions unhandled. Two of the most common and strong manifestations of such unhandled interactions are self-occlusion among the parts and the confusion in the localization of the non-adjacent sym...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	38,717
2012.03238	Fourier-domain Variational Formulation and Its Well-posedness for Supervised Learning	A supervised learning problem is to find a function in a hypothesis function space given values on isolated data points. Inspired by the frequency principle in neural networks, we propose a Fourier-domain variational formulation for supervised learning problem. This formulation circumvents the difficulty of imposing th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	210,044
2308.01469	VertexSerum: Poisoning Graph Neural Networks for Link Inference	Graph neural networks (GNNs) have brought superb performance to various applications utilizing graph structural data, such as social analysis and fraud detection. The graph links, e.g., social relationships and transaction history, are sensitive and valuable information, which raises privacy concerns when using GNNs. T...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	383,257
1909.13783	Optimal Periodic Multi-Agent Persistent Monitoring of a Finite Set of Targets with Uncertain States	We investigate the problem of persistently monitoring a finite set of targets with internal states that evolve with linear stochastic dynamics using a finite set of mobile agents. We approach the problem from the infinite-horizon perspective, looking for periodic movement schedules for the agents. Under linear dynamics...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	147,517
2306.00858	Adversarial learning of neural user simulators for dialogue policy optimisation	Reinforcement learning based dialogue policies are typically trained in interaction with a user simulator. To obtain an effective and robust policy, this simulator should generate user behaviour that is both realistic and varied. Current data-driven simulators are trained to accurately model the user behaviour in a dia...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	370,190
1910.13181	Bridging the ELBO and MMD	One of the challenges in training generative models such as the variational auto encoder (VAE) is avoiding posterior collapse. When the generator has too much capacity, it is prone to ignoring latent code. This problem is exacerbated when the dataset is small, and the latent dimension is high. The root of the problem i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,320
2202.03841	Width is Less Important than Depth in ReLU Neural Networks	We solve an open question from Lu et al. (2017), by showing that any target network with inputs in $\mathbb{R}^d$ can be approximated by a width $O(d)$ network (independent of the target network's architecture), whose number of parameters is essentially larger only by a linear factor. In light of previous depth separat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	279,359
2008.07235	A Survey of Deep Learning for Data Caching in Edge Network	The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	192,039
2411.03376	An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Services	This article presents the design of an open-API-based explainable AI (XAI) service to provide feature contribution explanations for cloud AI services. Cloud AI services are widely used to develop domain-specific applications with precise learning metrics. However, the underlying cloud AI services remain opaque on how t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	505,882
2408.12483	Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation	Dataset Distillation (DD) aims to synthesize a small dataset capable of performing comparably to the original dataset. Despite the success of numerous DD methods, theoretical exploration of this area remains unaddressed. In this paper, we take an initial step towards understanding various matching-based DD methods from...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	482,754
2110.04372	Fair Regression under Sample Selection Bias	Recent research on fair regression focused on developing new fairness notions and approximation methods as target variables and even the sensitive attribute are continuous in the regression setting. However, all previous fair regression research assumed the training data and testing data are drawn from the same distrib...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	259,856
2301.08360	Domain-adapted Learning and Imitation: DRL for Power Arbitrage	In this paper, we discuss the Dutch power market, which is comprised of a day-ahead market and an intraday balancing market that operates like an auction. Due to fluctuations in power supply and demand, there is often an imbalance that leads to different prices in the two markets, providing an opportunity for arbitrage...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	341,171
2405.00977	Distillation for Multilingual Information Retrieval	Recent work in cross-language information retrieval (CLIR), where queries and documents are in different languages, has shown the benefit of the Translate-Distill framework that trains a cross-language neural dual-encoder model using translation and distillation. However, Translate-Distill only supports a single docume...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	451,159
2405.05811	Parallel Cross Strip Attention Network for Single Image Dehazing	The objective of single image dehazing is to restore hazy images and produce clear, high-quality visuals. Traditional convolutional models struggle with long-range dependencies due to their limited receptive field size. While Transformers excel at capturing such dependencies, their quadratic computational complexity in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	453,071
1904.06197	Simulation of hyperelastic materials in real-time using Deep Learning	The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, adaptive meshing, and model order reduction. In this paper ...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	127,496
2312.01583	Efficient Collision Detection Oriented Motion Primitives for Path Planning	Mobile robots in dynamic environments require fast planning, especially when onboard computational resources are limited. While classic potential field based algorithms may suffice in simple scenarios, in most cases algorithms able to escape local minima are necessary. Configuration-space search algorithms have proven ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	412,503
1810.05723	Post-training 4-bit quantization of convolution networks for rapid-deployment	Convolutional neural networks require significant memory bandwidth and storage for intermediate computations, apart from substantial computing resources. Neural network quantization has significant benefits in reducing the amount of intermediate results, but it often requires the full datasets and time-consuming fine t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	110,289
2301.13284	Passively Addressed Robotic Morphing Surface (PARMS) Based on Machine Learning	Reconfigurable morphing surfaces provide new opportunities for advanced human-machine interfaces and bio-inspired robotics. Morphing into arbitrary surfaces on demand requires a device with a sufficiently large number of actuators and an inverse control strategy that can calculate the actuator stimulation necessary to ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	342,837
2109.08933	Optimization-based Block Coordinate Gradient Coding	Existing gradient coding schemes introduce identical redundancy across the coordinates of gradients and hence cannot fully utilize the computation results from partial stragglers. This motivates the introduction of diverse redundancies across the coordinates of gradients. This paper considers a distributed computation ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	256,082
2409.03203	An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification	Sentiment classification (SC) often suffers from low-resource challenges such as domain-specific contexts, imbalanced label distributions, and few-shot scenarios. The potential of the diffusion language model (LM) for textual data augmentation (DA) remains unexplored, moreover, textual DA methods struggle to balance th...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	485,958
1002.4935	Multiarray Signal Processing: Tensor decomposition meets compressed sensing	We discuss how recently discovered techniques and tools from compressed sensing can be used in tensor decompositions, with a view towards modeling signals from multiple arrays of multiple sensors. We show that with appropriate bounds on a measure of separation between radiating sources called coherence, one could alway...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,791
2005.08081	Rethinking and Improving Natural Language Generation with Layer-Wise Multi-View Decoding	In sequence-to-sequence learning, e.g., natural language generation, the decoder relies on the attention mechanism to efficiently extract information from the encoder. While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	177,496
2311.10572	SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning	Semi-supervised learning (SSL) methods effectively leverage unlabeled data to improve model generalization. However, SSL models often underperform in open-set scenarios, where unlabeled data contain outliers from novel categories that do not appear in the labeled set. In this paper, we study the challenging and realist...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	408,570
1710.04924	Two-stage Algorithm for Fairness-aware Machine Learning	Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	82,553
1903.12136	Distilling Task-Specific Knowledge from BERT into Simple Neural Networks	In the natural language processing literature, neural networks are becoming increasingly deeper and complex. The recent poster child of this trend is the deep language representation model, which includes BERT, ELMo, and GPT. These developments have led to the conviction that previous-generation, shallower neural netwo...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	125,651
2306.14924	LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding	Deductive coding is a widely used qualitative research method for determining the prevalence of themes across documents. While useful, deductive coding is often burdensome and time consuming since it requires researchers to read, interpret, and reliably categorize a large body of unstructured text documents. Large lang...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	375,870
2403.09702	Generator-Guided Crowd Reaction Assessment	In the realm of social media, understanding and predicting post reach is a significant challenge. This paper presents a Crowd Reaction AssessMent (CReAM) task designed to estimate if a given social media post will receive more reaction than another, a particularly essential task for digital marketers and content writer...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	437,871
2203.07191	Impedance Adaptation by Reinforcement Learning with Contact Dynamic Movement Primitives	Dynamic movement primitives (DMPs) allow complex position trajectories to be efficiently demonstrated to a robot. In contact-rich tasks, where position trajectories alone may not be safe or robust over variation in contact geometry, DMPs have been extended to include force trajectories. However, different task phases o...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	285,364
2401.04368	Enhancing Acute Kidney Injury Prediction through Integration of Drug Features in Intensive Care Units	The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	420,412
2002.06873	$\pi$VAE: a stochastic process prior for Bayesian deep learning with MCMC	Stochastic processes provide a mathematically elegant way model complex data. In theory, they provide flexible priors over function classes that can encode a wide range of interesting assumptions. In practice, however, efficient inference by optimisation or marginalisation is difficult, a problem further exacerbated wi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,331
2205.11993	Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNs	This work proposes an algorithm for fMRI data analysis for the classification of ADHD disorders. There have been several breakthroughs in the analysis of fMRI via 3D convolutional neural networks (CNNs). With these new techniques it is possible to preserve the 3D spatial data of fMRI data. Additionally there have been ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	298,364
2312.16364	Robustness Verification for Knowledge-Based Logic of Risky Driving Scenes	Many decision-making scenarios in modern life benefit from the decision support of artificial intelligence algorithms, which focus on a data-driven philosophy and automated programs or systems. However, crucial decision issues related to security, fairness, and privacy should consider more human knowledge and principle...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	418,362
2011.01103	Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain	The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological inf...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	204,482
1809.08567	Identification and Visualization of the Underlying Independent Causes of the Diagnostic of Diabetic Retinopathy made by a Deep Learning Classifier	Interpretability is a key factor in the design of automatic classifiers for medical diagnosis. Deep learning models have been proven to be a very effective classification algorithm when trained in a supervised way with enough data. The main concern is the difficulty of inferring rationale interpretations from them. Dif...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,536
2004.01738	Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction	Many real-world signal sources are complex-valued, having real and imaginary components. However, the vast majority of existing deep learning platforms and network architectures do not support the use of complex-valued data. MRI data is inherently complex-valued, so existing approaches discard the richer algebraic stru...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	170,994
1611.00123	Interference-Constrained Pricing for D2D Networks	The concept of device-to-device (D2D) communications underlaying cellular networks opens up potential benefits for improving system performance but also brings new challenges such as interference management. In this paper, we propose a pricing framework for interference management from the D2D users to the cellular sys...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	63,171
2210.11698	Learning Robust Dynamics through Variational Sparse Gating	Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes. World models have previously been shown to improve sample-efficiency in simulated environments with few objects, but have not yet been applied successfully to environments with many objects. In environ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	325,416
2011.02073	Optimal Control-Based Baseline for Guided Exploration in Policy Gradient Methods	In this paper, a novel optimal control-based baseline function is presented for the policy gradient method in deep reinforcement learning (RL). The baseline is obtained by computing the value function of an optimal control problem, which is formed to be closely associated with the RL task. In contrast to the traditiona...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	204,810
2308.14474	Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FI	In the current ML field models are getting larger and more complex, and data used for model training are also getting larger in quantity and higher in dimensions. Therefore, in order to train better models, and save training time and computational resources, a good Feature Selection (FS) method in the preprocessing sta...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	388,341
2103.00451	Discovering Dense Correlated Subgraphs in Dynamic Networks	Given a dynamic network, where edges appear and disappear over time, we are interested in finding sets of edges that have similar temporal behavior and form a dense subgraph. Formally, we define the problem as the enumeration of the maximal subgraphs that satisfy specific density and similarity thresholds. To measure t...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	222,283
1905.11065	Infinitely deep neural networks as diffusion processes	When the parameters are independently and identically distributed (initialized) neural networks exhibit undesirable properties that emerge as the number of layers increases, e.g. a vanishing dependency on the input and a concentration on restrictive families of functions including constant functions. We consider parame...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,320
1809.06746	Bridging the Gap Between Safety and Real-Time Performance in Receding-Horizon Trajectory Design for Mobile Robots	To operate with limited sensor horizons in unpredictable environments, autonomous robots use a receding-horizon strategy to plan trajectories, wherein they execute a short plan while creating the next plan. However, creating safe, dynamically-feasible trajectories in real time is challenging; and, planners must ensure ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	108,134
2309.08957	ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images	We present ExBluRF, a novel view synthesis method for extreme motion blurred images based on efficient radiance fields optimization. Our approach consists of two main components: 6-DOF camera trajectory-based motion blur formulation and voxel-based radiance fields. From extremely blurred images, we optimize the sharp r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	392,408
1801.07593	Mitigating Unwanted Biases with Adversarial Learning	Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases. We present a framework for mitigating such biases by including a variable for the group of interest a...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	88,813
2312.15521	BP-MPC: Optimizing the Closed-Loop Performance of MPC using BackPropagation	Model predictive control (MPC) is pervasive in research and industry. However, designing the cost function and the constraints of the MPC to maximize closed-loop performance remains an open problem. To achieve optimal tuning, we propose a backpropagation scheme that solves a policy optimization problem with nonlinear s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	418,037

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