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
1801.07733	On the Key Generation Rate of Physically Unclonable Functions	In this paper, an algebraic binning based coding scheme and its associated achievable rate for key generation using physically unclonable functions (PUFs) is determined. This achievable rate is shown to be optimal under the generated-secret (GS) model for PUFs. Furthermore, a polar code based polynomial-time encoding a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	88,835
2207.07895	JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes	Depth estimation, visual odometry (VO), and bird's-eye-view (BEV) scene layout estimation present three critical tasks for driving scene perception, which is fundamental for motion planning and navigation in autonomous driving. Though they are complementary to each other, prior works usually focus on each individual ta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,362
2302.02833	What may future electricity markets look like?	Should the organization, design and functioning of electricity markets be taken for granted? Definitely not. While decades of evolution of electricity markets in countries that committed early to restructure their electric power sector made us believe that we may have found the right and future-proof model, the substan...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	344,126
1911.12780	Detection and Mitigation of Rare Subclasses in Deep Neural Network Classifiers	Regions of high-dimensional input spaces that are underrepresented in training datasets reduce machine-learnt classifier performance, and may lead to corner cases and unwanted bias for classifiers used in decision making systems. When these regions belong to otherwise well-represented classes, their presence and negati...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	155,489
2005.10718	An Importance Aware Weighted Coding Theorem Using Message Importance Measure	There are numerous scenarios in source coding where not only the code length but the importance of each value should also be taken into account. Different from the traditional coding theorems, by adding the importance weights for the length of the codes, we define the average cost of the weighted codeword length as an ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	178,267
2103.01205	Statistically Significant Stopping of Neural Network Training	The general approach taken when training deep learning classifiers is to save the parameters after every few iterations, train until either a human observer or a simple metric-based heuristic decides the network isn't learning anymore, and then backtrack and pick the saved parameters with the best validation accuracy. ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	222,542
1002.2050	Intrinsic dimension estimation of data by principal component analysis	Estimating intrinsic dimensionality of data is a classic problem in pattern recognition and statistics. Principal Component Analysis (PCA) is a powerful tool in discovering dimensionality of data sets with a linear structure; it, however, becomes ineffective when data have a nonlinear structure. In this paper, we propo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	5,668
2402.14523	Daisy-TTS: Simulating Wider Spectrum of Emotions via Prosody Embedding Decomposition	We often verbally express emotions in a multifaceted manner, they may vary in their intensities and may be expressed not just as a single but as a mixture of emotions. This wide spectrum of emotions is well-studied in the structural model of emotions, which represents variety of emotions as derivative products of prima...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	431,722
2409.06177	HierLLM: Hierarchical Large Language Model for Question Recommendation	Question recommendation is a task that sequentially recommends questions for students to enhance their learning efficiency. That is, given the learning history and learning target of a student, a question recommender is supposed to select the question that will bring the most improvement for students. Previous methods ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	487,025
2407.20485	A2SF: Accumulative Attention Scoring with Forgetting Factor for Token Pruning in Transformer Decoder	Recently, large language models (LLM) based on transformers are facing memory bottleneck issues due to KV cache, especially in long sequence handling. Previous researches proposed KV cache compression techniques that identify insignificant tokens based on Accumulative Attention Scores and removes their items from KV ca...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	477,177
1611.02779	RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning	Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world. This paper seeks to bridge th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	63,610
2310.04469	Taming Binarized Neural Networks and Mixed-Integer Programs	There has been a great deal of recent interest in binarized neural networks, especially because of their explainability. At the same time, automatic differentiation algorithms such as backpropagation fail for binarized neural networks, which limits their applicability. By reformulating the problem of training binarized...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	397,674
2412.09585	OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation	The standard practice for developing contemporary MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision. In this work, we posit an overlooked opportunity to optimize the intermediate LLM representations through a vision perspective (objective), i.e., solely natural la...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	516,540
1711.08281	Analysis of atmospheric effects on satellite based quantum communication: A comparative study	Quantum Key Distribution (QKD) is a key exchange protocol which is implemented over free space optical links and optical fiber cable. When direct communication is not possible, QKD is performed over fiber cables, but the imperfections in detectors used at receiver side and also the material properties of fiber cables l...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	85,178
1909.01524	Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion	Gross tumor volume (GTV) segmentation is a critical step in esophageal cancer radiotherapy treatment planning. Inconsistencies across oncologists and prohibitive labor costs motivate automated approaches for this task. However, leading approaches are only applied to radiotherapy computed tomography (RTCT) images taken ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	143,927
2311.17932	Swallowing the Bitter Pill: Simplified Scalable Conformer Generation	We present a novel way to predict molecular conformers through a simple formulation that sidesteps many of the heuristics of prior works and achieves state of the art results by using the advantages of scale. By training a diffusion generative model directly on 3D atomic positions without making assumptions about the e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	411,473
2303.14175	Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation	Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations. In this paper, we propose a novel Inherent Consistent Learning (ICL) method, aims to learn robust semantic category representations through the semantic consistency guidance of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,973
2004.14415	Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin Liquids	Kitaev materials are promising materials for hosting quantum spin liquids and investigating the interplay of topological and symmetry-breaking phases. We use an unsupervised and interpretable machine-learning method, the tensorial-kernel support vector machine, to study the honeycomb Kitaev-$\Gamma$ model in a magnetic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	174,875
2105.05600	ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion	Online reconstruction based on RGB-D sequences has thus far been restrained to relatively slow camera motions (<1m/s). Under very fast camera motion (e.g., 3m/s), the reconstruction can easily crumble even for the state-of-the-art methods. Fast motion brings two challenges to depth fusion: 1) the high nonlinearity of c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	234,864
2407.16036	Transformer-based Capacity Prediction for Lithium-ion Batteries with Data Augmentation	Lithium-ion batteries are pivotal to technological advancements in transportation, electronics, and clean energy storage. The optimal operation and safety of these batteries require proper and reliable estimation of battery capacities to monitor the state of health. Current methods for estimating the capacities fail to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	475,430
1010.5412	On optimizing over lift-and-project closures	The lift-and-project closure is the relaxation obtained by computing all lift-and-project cuts from the initial formulation of a mixed integer linear program or equivalently by computing all mixed integer Gomory cuts read from all tableau's corresponding to feasible and infeasible bases. In this paper, we present an al...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	8,024
2408.11656	Macformer: Transformer with Random Maclaurin Feature Attention	Random feature attention (RFA) adopts random fourier feature (RFF) methods to approximate the softmax function, resulting in a linear time and space attention mechanism that enables the construction of an efficient Transformer. Inspired by RFA, we propose Macformer, a Transformer architecture that employs random Maclau...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	482,376
1303.6135	Model-Based Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing	The random demodulator is a recent compressive sensing architecture providing efficient sub-Nyquist sampling of sparse band-limited signals. The compressive sensing paradigm requires an accurate model of the analog front-end to enable correct signal reconstruction in the digital domain. In practice, hardware devices su...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,248
1710.04012	Marine Wireless Big Data: Efficient Transmission, Related Applications, and Challenges	The vast volume of marine wireless sampling data and its continuously explosive growth herald the coming of the era of marine wireless big data. Two challenges imposed by these data are how to fast, reliably, and sustainably deliver them in extremely hostile marine environments and how to apply them after collection. I...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	82,409
2402.02662	Image-Caption Encoding for Improving Zero-Shot Generalization	Recent advances in vision-language models have combined contrastive approaches with generative methods to achieve state-of-the-art (SOTA) on downstream inference tasks like zero-shot image classification. However, a persistent issue of these models for image classification is their out-of-distribution (OOD) generalizat...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	426,674
2407.16829	PlantTrack: Task-Driven Plant Keypoint Tracking with Zero-Shot Sim2Real Transfer	Tracking plant features is crucial for various agricultural tasks like phenotyping, pruning, or harvesting, but the unstructured, cluttered, and deformable nature of plant environments makes it a challenging task. In this context, the recent advancements in foundational models show promise in addressing this challenge....	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	475,738
2502.11469	If Attention Serves as a Cognitive Model of Human Memory Retrieval, What is the Plausible Memory Representation?	Recent work in computational psycholinguistics has revealed intriguing parallels between attention mechanisms and human memory retrieval, focusing primarily on Transformer architectures that operate on token-level representations. However, computational psycholinguistic research has also established that syntactic stru...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	534,391
2305.03567	Flash: An Asynchronous Payment System with Good-Case Linear Communication Complexity	While the original purpose of blockchains was to realize a payment system, it has been shown that, in fact, such systems do not require consensus and can be implemented deterministically in asynchronous networks. State-of-the-art payment systems employ Reliable Broadcast to disseminate payments and prevent double spend...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	362,441
2306.05989	QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective RAN KPI Forecasting	Forecasting time series patterns, such as cell key performance indicators (KPIs) of radio access networks (RAN), plays a vital role in enhancing service quality and operational efficiency. State-of-the-art forecasting approaches prioritize accuracy at the expense of computational performance, rendering them less suitab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	372,397
2201.10685	Design and Development of an Autonomous Surface Vehicle for Water Quality Monitoring	Manually monitoring water quality is very exhausting and requires several hours of sampling and laboratory testing for a particular body of water. This article presents a solution to test water properties like electrical conductivity and pH with a remote-controlled floating vehicle that minimizes time intervals. An aut...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	277,068
2403.06639	Robust and fast backbone tracking via phase-locked loops	Phase-locked loops are commonly used for shaker-based backbone tracking of nonlinear structures. The state of the art is to tune the control parameters by trial and error. In the present work, an approach is proposed to make backbone tracking much more robust and faster. A simple PI controller is proposed, and closed-f...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	436,542
2310.11886	Sampling Algorithms for Butterfly Counting on Temporal Bipartite Graphs	Temporal bipartite graphs are widely used to denote time-evolving relationships between two disjoint sets of nodes, such as customer-product interactions in E-commerce and user-group memberships in social networks. Temporal butterflies, $(2,2)$-bicliques that occur within a short period and in a prescribed order, are e...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	true	400,826
2310.06266	CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model	Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the effectiveness of existing models in understanding non-English inputs for multi-lingual code-related tasks is still far from well studied. Th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	398,499
2409.00620	Enhancing Vectorized Map Perception with Historical Rasterized Maps	In autonomous driving, there is growing interest in end-to-end online vectorized map perception in bird's-eye-view (BEV) space, with an expectation that it could replace traditional high-cost offline high-definition (HD) maps. However, the accuracy and robustness of these methods can be easily compromised in challengin...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	485,001
1812.04418	Towards Automatic Identification of Elephants in the Wild	Identifying animals from a large group of possible individuals is very important for biodiversity monitoring and especially for collecting data on a small number of particularly interesting individuals, as these have to be identified first before this can be done. Identifying them can be a very time-consuming task. Thi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	116,214
2210.00301	Learning Globally Smooth Functions on Manifolds	Smoothness and low dimensional structures play central roles in improving generalization and stability in learning and statistics. This work combines techniques from semi-infinite constrained learning and manifold regularization to learn representations that are globally smooth on a manifold. To do so, it shows that un...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	320,815
2311.03401	Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach	Research in scientific disciplines evolves, often rapidly, over time with the emergence of novel methodologies and their associated terminologies. While methodologies themselves being conceptual in nature and rather difficult to automatically extract and characterise, in this paper, we seek to develop supervised models...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	405,841
2211.04393	Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts	Improving model's generalizability against domain shifts is crucial, especially for safety-critical applications such as autonomous driving. Real-world domain styles can vary substantially due to environment changes and sensor noises, but deep models only know the training domain style. Such domain style gap impedes mo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	329,231
1712.02854	Stochastic reconstruction of an oolitic limestone by generative adversarial networks	Stochastic image reconstruction is a key part of modern digital rock physics and materials analysis that aims to create numerous representative samples of material micro-structures for upscaling, numerical computation of effective properties and uncertainty quantification. We present a method of three-dimensional stoch...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,346
2006.06815	Discussing Privacy and Surveillance on Twitter: A Case Study of COVID-19	Technology is uniquely positioned to help us analyze large amounts of information to provide valuable insight during widespread public health concerns, like the ongoing COVID-19 pandemic. In fact, information technology companies like Apple and Google have recently launched tools for contact tracing-the ability to proc...	false	false	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	false	181,561
1603.02763	megaman: Manifold Learning with Millions of points	Manifold Learning is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data. Thus manifold learning algorithms are, at least in theory, most applicable to high-dimensional data and sample sizes to enable accurate estimation of the manifold. Despite this, most existing manifol...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	53,053
2204.01800	The Fast Johnson-Lindenstrauss Transform is Even Faster	The seminal Fast Johnson-Lindenstrauss (Fast JL) transform by Ailon and Chazelle (SICOMP'09) embeds a set of $n$ points in $d$-dimensional Euclidean space into optimal $k=O(\varepsilon^{-2} \ln n)$ dimensions, while preserving all pairwise distances to within a factor $(1 \pm \varepsilon)$. The Fast JL transform suppor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	289,730
2502.12175	Spatiotemporal Graph Neural Networks in short term load forecasting: Does adding Graph Structure in Consumption Data Improve Predictions?	Short term Load Forecasting (STLF) plays an important role in traditional and modern power systems. Most STLF models predominantly exploit temporal dependencies from historical data to predict future consumption. Nowadays, with the widespread deployment of smart meters, their data can contain spatiotemporal dependencie...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	534,730
1811.03815	Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer Images	In this paper, we develop a complete pipeline for stain normalization, segmentation, and classification of nuclei in hematoxylin and eosin (H&E) stained breast cancer histopathology images. In the first step, we use a CNN-based stain transfer technique to normalize the staining characteristics of (H&E) images. We then ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	112,935
1911.02399	Dynamic Energy Beacon: An Adaptive and Cost-effective Energy Harvesting and Power Management System for A Better Life	In this proposal, a cost-effective energy harvesting and management system have been proposed. The regular power keeps around 200 Watt while the peak power can reach 300 Watt. The cost of this system satisfies the requirements and budget for residents in the rural area and live off-grid. It could be a potential solutio...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	152,353
0911.5395	An axiomatic approach to the roughness measure of rough sets	In Pawlak's rough set theory, a set is approximated by a pair of lower and upper approximations. To measure numerically the roughness of an approximation, Pawlak introduced a quantitative measure of roughness by using the ratio of the cardinalities of the lower and upper approximations. Although the roughness measure i...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	5,039
2105.01502	Technical Report for Valence-Arousal Estimation on Affwild2 Dataset	In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW FG-2020 Competition. The competition organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze affective behavior in real-life settings. We use MIMAMO Net \cite{deng2020mimamo} model to achieve inf...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,540
2102.04490	Unsupervised Abstractive Summarization of Bengali Text Documents	Abstractive summarization systems generally rely on large collections of document-summary pairs. However, the performance of abstractive systems remains a challenge due to the unavailability of parallel data for low-resource languages like Bengali. To overcome this problem, we propose a graph-based unsupervised abstrac...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	219,128
1608.06557	Neural Networks with Smooth Adaptive Activation Functions for Regression	In Neural Networks (NN), Adaptive Activation Functions (AAF) have parameters that control the shapes of activation functions. These parameters are trained along with other parameters in the NN. AAFs have improved performance of Neural Networks (NN) in multiple classification tasks. In this paper, we propose and apply A...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,128
2305.12707	Quantifying Association Capabilities of Large Language Models and Its Implications on Privacy Leakage	The advancement of large language models (LLMs) brings notable improvements across various applications, while simultaneously raising concerns about potential private data exposure. One notable capability of LLMs is their ability to form associations between different pieces of information, but this raises concerns whe...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	366,142
2209.03299	Multimodal learning with graphs	Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics. However, the increasingly heterogeneous graph datasets call for multimodal methods that can combine different inductive biases: the set of ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	316,462
1903.00520	A Reachability Method for Verifying Dynamical Systems with Deep Neural Network Controllers	Deep neural networks can be trained to be efficient and effective controllers for dynamical systems; however, the mechanics of deep neural networks are complex and difficult to guarantee. This work presents a general approach for providing guarantees for deep neural network controllers over multiple time steps using a ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	123,030
1506.01125	Unsupervised domain adaption dictionary learning for visual recognition	Over the last years, dictionary learning method has been extensively applied to deal with various computer vision recognition applications, and produced state-of-the-art results. However, when the data instances of a target domain have a different distribution than that of a source domain, the dictionary learning metho...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	43,764
2102.11907	A predictive safety filter for learning-based racing control	The growing need for high-performance controllers in safety-critical applications like autonomous driving has been motivating the development of formal safety verification techniques. In this paper, we design and implement a predictive safety filter that is able to maintain vehicle safety with respect to track boundari...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	221,552
2304.01285	X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs	Structured, or tabular, data is the most common format in data science. While deep learning models have proven formidable in learning from unstructured data such as images or speech, they are less accurate than simpler approaches when learning from tabular data. In contrast, modern tree-based Machine Learning (ML) mode...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	356,007
2212.12653	Hyperspherical Quantization: Toward Smaller and More Accurate Models	Model quantization enables the deployment of deep neural networks under resource-constrained devices. Vector quantization aims at reducing the model size by indexing model weights with full-precision embeddings, i.e., codewords, while the index needs to be restored to 32-bit during computation. Binary and other low-pre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	338,089
1802.08768	Is Generator Conditioning Causally Related to GAN Performance?	Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning. Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs). We find th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,176
0907.3574	Message Passing Algorithms for Compressed Sensing	Compressed sensing aims to undersample certain high-dimensional signals, yet accurately reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible when the object to be recovered is sufficiently sparse in a known basis. Currently, the best known sparsity-undersampling tradeoff is achieve...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,138
1507.01066	Graphulo Implementation of Server-Side Sparse Matrix Multiply in the Accumulo Database	The Apache Accumulo database excels at distributed storage and indexing and is ideally suited for storing graph data. Many big data analytics compute on graph data and persist their results back to the database. These graph calculations are often best performed inside the database server. The GraphBLAS standard provide...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	44,816
2401.11814	Symbrain: A large-scale dataset of MRI images for neonatal brain symmetry analysis	This paper presents an annotated dataset of brain MRI images designed to advance the field of brain symmetry study. Magnetic resonance imaging (MRI) has gained interest in analyzing brain symmetry in neonatal infants, and challenges remain due to the vast size differences between fetal and adult brains. Classification ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	423,159
1802.03573	Social Media, News and Political Information during the US Election: Was Polarizing Content Concentrated in Swing States?	US voters shared large volumes of polarizing political news and information in the form of links to content from Russian, WikiLeaks and junk news sources. Was this low quality political information distributed evenly around the country, or concentrated in swing states and particular parts of the country? In this data m...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	90,009
2109.03331	CyGIL: A Cyber Gym for Training Autonomous Agents over Emulated Network Systems	Given the success of reinforcement learning (RL) in various domains, it is promising to explore the application of its methods to the development of intelligent and autonomous cyber agents. Enabling this development requires a representative RL training environment. To that end, this work presents CyGIL: an experimenta...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	254,024
1906.00743	Distributed Uplink Power Control in an Ultra-Dense Millimeter Wave Network: A Mean-field Game Approach	In this paper, a novel mean-field game framework is proposed for uplink power control in an ultra-dense millimeter wave network. The proposed mean-field game considers the time evolution of the mobile users' orientations as well as the energy available in their batteries, under adaptive user association. The objective ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	133,498
2211.10585	Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving	Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles (HVs) is challenging, as HVs continuously update their policies in response to AVs. In order to navigate safely in the presence of complex AV-HV social interactions, the AVs must learn to predict these changes. Humans are capable of navigating ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	331,356
2409.08811	Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task	Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others. When AI agents with ToM capability collaborate with humans, Mutual Theory of Mind (MToM) arises in such human-AI teams (HATs). The MToM process, which involves interactive communication and ToM...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	488,067
2110.03384	Deep Learning Model Explainability for Inspection Accuracy Improvement in the Automotive Industry	The welding seams visual inspection is still manually operated by humans in different companies, so the result of the test is still highly subjective and expensive. At present, the integration of deep learning methods for welds classification is a research focus in engineering applications. This work intends to apprehe...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	259,480
2208.06267	Causal Imitation Learning with Unobserved Confounders	One of the common ways children learn is by mimicking adults. Imitation learning focuses on learning policies with suitable performance from demonstrations generated by an expert, with an unspecified performance measure, and unobserved reward signal. Popular methods for imitation learning start by either directly mimic...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	312,660
2310.12063	Black-Box Training Data Identification in GANs via Detector Networks	Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given access to a trained GAN, as well as fresh samples from the underlying distribution, if it is possible for an attacker to efficiently identi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	400,890
2312.03011	InstructBooth: Instruction-following Personalized Text-to-Image Generation	Personalizing text-to-image models using a limited set of images for a specific object has been explored in subject-specific image generation. However, existing methods often face challenges in aligning with text prompts due to overfitting to the limited training images. In this work, we introduce InstructBooth, a nove...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	413,090
1710.03189	Towards Agent-Based Model Specification in Smart Grid: A Cognitive Agent-based Computing Approach	A smart grid can be considered as a complex network where each node represents a generation unit or a consumer. Whereas links can be used to represent transmission lines. One way to study complex systems is by using the agent-based modeling (ABM) paradigm. An ABM is a way of representing a complex system of autonomous ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	82,289
2306.03466	Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems	Plug-and-Play (PnP) methods are efficient iterative algorithms for solving ill-posed image inverse problems. PnP methods are obtained by using deep Gaussian denoisers instead of the proximal operator or the gradient-descent step within proximal algorithms. Current PnP schemes rely on data-fidelity terms that have eithe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	371,344
2204.11385	DRT: A Lightweight Single Image Deraining Recursive Transformer	Over parameterization is a common technique in deep learning to help models learn and generalize sufficiently to the given task; nonetheless, this often leads to enormous network structures and consumes considerable computing resources during training. Recent powerful transformer-based deep learning models on vision ta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	293,125
2312.14260	Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience	Machine learning models are being used in an increasing number of critical applications; thus, securing their integrity and ownership is critical. Recent studies observed that adversarial training and watermarking have a conflicting interaction. This work introduces a novel framework to integrate adversarial training w...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	417,567
2201.04234	Leveraging Unlabeled Data to Predict Out-of-Distribution Performance	Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions that may cause performance drops. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Aver...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	275,049
2111.00500	DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention	Object detection often costs a considerable amount of computation to get satisfied performance, which is unfriendly to be deployed in edge devices. To address the trade-off between computational cost and detection accuracy, this paper presents a dual path network, named DPNet, for efficient object detection with lightw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	264,237
2407.04118	MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization	Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research primarily emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. However, a good prompt is not solely define...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	470,435
1808.09129	Random Matrices from Linear Codes and Wigner's semicircle law	In this paper we consider a new normalization of matrices obtained by choosing distinct codewords at random from linear codes over finite fields and find that under some natural algebraic conditions of the codes their empirical spectral distribution converges to Wigner's semicircle law as the length of the codes goes t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	106,121
2111.00009	Revisiting joint decoding based multi-talker speech recognition with DNN acoustic model	In typical multi-talker speech recognition systems, a neural network-based acoustic model predicts senone state posteriors for each speaker. These are later used by a single-talker decoder which is applied on each speaker-specific output stream separately. In this work, we argue that such a scheme is sub-optimal and pr...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,064
2109.00393	Mean absorption estimation from room impulse responses using virtually supervised learning	In the context of building acoustics and the acoustic diagnosis of an existing room, this paper introduces and investigates a new approach to estimate mean absorption coefficients solely from a room impulse response (RIR). This inverse problem is tackled via virtually-supervised learning, namely, the RIR-to-absorption ...	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	253,091
2410.05787	An accelerate Prediction Strategy for Dynamic Multi-Objective Optimization	This paper addresses the challenge of dynamic multi-objective optimization problems (DMOPs) by introducing novel approaches for accelerating prediction strategies within the evolutionary algorithm framework. Since the objectives of DMOPs evolve over time, both the Pareto optimal set (PS) and the Pareto optimal front (P...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	495,916
1608.07802	MindX: Denoising Mixed Impulse Poisson-Gaussian Noise Using Proximal Algorithms	We present a novel algorithm for blind denoising of images corrupted by mixed impulse, Poisson, and Gaussian noises. The algorithm starts by applying the Anscombe variance-stabilizing transformation to convert the Poisson into white Gaussian noise. Then it applies a combinatorial optimization technique to denoise the m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,272
2009.00206	RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation	We present RangeRCNN, a novel and effective 3D object detection framework based on the range image representation. Most existing methods are voxel-based or point-based. Though several optimizations have been introduced to ease the sparsity issue and speed up the running time, the two representations are still computati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,984
1711.04340	Data Augmentation Generative Adversarial Networks	Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation alleviates this by using existing data more effectively. However standard data augmentation produces only limited plausible alternative data. Given t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	84,381
2305.01567	Teaching data-driven control: from linear design to adaptive control with throttle valves	Electric throttle valves represent a challenge for control design, as their dynamics involve strong nonlinearities, characterized by an asymmetric hysteresis. Carrying experiments on multiple valves, a large variability in the characteristics of each valve and erratic steady-state behaviors can also be noticed, impairi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	361,719
2308.13616	Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach	Channel estimation in reconfigurable intelligent surfaces (RIS)-aided systems is crucial for optimal configuration of the RIS and various downstream tasks such as user localization. In RIS-aided systems, channel estimation involves estimating two channels for the user-RIS (UE-RIS) and RIS-base station (RIS-BS) links. I...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	387,988
2310.00923	Lightweight Regression Model with Prediction Interval Estimation for Computer Vision-based Winter Road Surface Condition Monitoring	Winter conditions pose several challenges for automated driving applications. A key challenge during winter is accurate assessment of road surface condition, as its impact on friction is a critical parameter for safely and reliably controlling a vehicle. This paper proposes a deep learning regression model, SIWNet, cap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	396,210
2007.05302	Topic Modeling on User Stories using Word Mover's Distance	Requirements elicitation has recently been complemented with crowd-based techniques, which continuously involve large, heterogeneous groups of users who express their feedback through a variety of media. Crowd-based elicitation has great potential for engaging with (potential) users early on but also results in large s...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	186,633
2407.10606	Visual-tactile manipulation to collect household waste in outdoor	This work presents a perception system applied to robotic manipulation, that is able to assist in navigation, household waste classification and collection in outdoor environments. This system is made up of optical tactile sensors, RGBD cameras and a LiDAR. These sensors are integrated on a mobile platform with a robot...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	473,048
2501.13987	OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting	Post-training quantization (PTQ) has emerged as a widely adopted technique for compressing and accelerating Large Language Models (LLMs). The major challenge in LLM quantization is that uneven and heavy-tailed data distributions can expand the quantization range, thereby reducing bit precision for most values. Recent m...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	526,930
1502.05615	Forgetting and consolidation for incremental and cumulative knowledge acquisition systems	The application of cognitive mechanisms to support knowledge acquisition is, from our point of view, crucial for making the resulting models coherent, efficient, credible, easy to use and understandable. In particular, there are two characteristic features of intelligence that are essential for knowledge development: f...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	40,387
2202.08333	Self-Supervised Representation Learning via Latent Graph Prediction	Self-supervised learning (SSL) of graph neural networks is emerging as a promising way of leveraging unlabeled data. Currently, most methods are based on contrastive learning adapted from the image domain, which requires view generation and a sufficient number of negative samples. In contrast, existing predictive model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	280,833
1602.01517	Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification	We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors. In many applications, especially including remote sensing, it is...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,707
1604.02416	How deep is knowledge tracing?	In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the l...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	54,330
2402.10951	DAEDRA: A language model for predicting outcomes in passive pharmacovigilance reporting	Over the recent years, the emergence of large language models (LLMs) has given rise to a proliferation of domain-specific models that are intended to reflect the particularities of linguistic context and content as a correlate of the originating domain. This paper details the conception, design, training and evaluation...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	430,179
2501.00418	Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models	The rapid proliferation of generative AI, especially large language models, has led to their integration into a variety of applications. A key phenomenon known as weak-to-strong generalization - where a strong model trained on a weak model's outputs surpasses the weak model in task performance - has gained significant ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	521,658
2110.08529	Sharpness-Aware Minimization Improves Language Model Generalization	The allure of superhuman-level capabilities has led to considerable interest in language models like GPT-3 and T5, wherein the research has, by and large, revolved around new model architectures, training tasks, and loss objectives, along with substantial engineering efforts to scale up model capacity and dataset size....	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	261,445
2306.07956	Adaptive Monte Carlo Search for Conjecture Refutation in Graph Theory	Graph theory is an interdisciplinary field of study that has various applications in mathematical modeling and computer science. Research in graph theory depends on the creation of not only theorems but also conjectures. Conjecture-refuting algorithms attempt to refute conjectures by searching for counterexamples to th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	373,216
1109.6029	An Improved Search Algorithm for Optimal Multiple-Sequence Alignment	Multiple sequence alignment (MSA) is a ubiquitous problem in computational biology. Although it is NP-hard to find an optimal solution for an arbitrary number of sequences, due to the importance of this problem researchers are trying to push the limits of exact algorithms further. Since MSA can be cast as a classical p...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	12,360
0906.1603	Multiaccess Channels with State Known to One Encoder: Another Case of Degraded Message Sets	We consider a two-user state-dependent multiaccess channel in which only one of the encoders is informed, non-causally, of the channel states. Two independent messages are transmitted: a common message transmitted by both the informed and uninformed encoders, and an individual message transmitted by only the uninformed...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,849
1905.04068	On the Distribution of AoI for the GI/GI/1/1 and GI/GI/1/2* Systems: Exact Expressions and Bounds	Since Age of Information (AoI) has been proposed as a metric that quantifies the freshness of information updates in a communication system, there has been a constant effort in understanding and optimizing different statistics of the AoI process for classical queueing systems. In addition to classical queuing systems, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	130,356

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.