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
1207.1420	Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars	This paper addresses the problem of mapping natural language sentences to lambda-calculus encodings of their meaning. We describe a learning algorithm that takes as input a training set of sentences labeled with expressions in the lambda calculus. The algorithm induces a grammar for the problem, along with a log-linear...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	17,300
2407.10281	Beyond Prompt Learning: Continual Adapter for Efficient Rehearsal-Free Continual Learning	The problem of Rehearsal-Free Continual Learning (RFCL) aims to continually learn new knowledge while preventing forgetting of the old knowledge, without storing any old samples and prototypes. The latest methods leverage large-scale pre-trained models as the backbone and use key-query matching to generate trainable pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,919
1708.02862	WebVision Database: Visual Learning and Understanding from Web Data	In this paper, we present a study on learning visual recognition models from large scale noisy web data. We build a new database called WebVision, which contains more than $2.4$ million web images crawled from the Internet by using queries generated from the 1,000 semantic concepts of the benchmark ILSVRC 2012 dataset....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,672
2408.03558	D2Styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods	In image processing, one of the most challenging tasks is to render an image's semantic meaning using a variety of artistic approaches. Existing techniques for arbitrary style transfer (AST) frequently experience mode-collapse, over-stylization, or under-stylization due to a disparity between the style and content imag...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	479,064
1906.03821	Time-Series Anomaly Detection Service at Microsoft	Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time. In this paper, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,511
2012.03378	Brain Co-Processors: Using AI to Restore and Augment Brain Function	Brain-computer interfaces (BCIs) use decoding algorithms to control prosthetic devices based on brain signals for restoration of lost function. Computer-brain interfaces (CBIs), on the other hand, use encoding algorithms to transform external sensory signals into neural stimulation patterns for restoring sensation or p...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	210,084
2402.02321	Active Learning for Graphs with Noisy Structures	Graph Neural Networks (GNNs) have seen significant success in tasks such as node classification, largely contingent upon the availability of sufficient labeled nodes. Yet, the excessive cost of labeling large-scale graphs led to a focus on active learning on graphs, which aims for effective data selection to maximize d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	426,492
2005.08650	Development of a New Image-to-text Conversion System for Pashto, Farsi and Traditional Chinese	We report upon the results of a research and prototype building project \emph{Worldly~OCR} dedicated to developing new, more accurate image-to-text conversion software for several languages and writing systems. These include the cursive scripts Farsi and Pashto, and Latin cursive scripts. We also describe approaches ge...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	177,696
1508.03660	Computing in Additive Networks with Bounded-Information Codes	This paper studies the theory of the additive wireless network model, in which the received signal is abstracted as an addition of the transmitted signals. Our central observation is that the crucial challenge for computing in this model is not high contention, as assumed previously, but rather guaranteeing a bounded a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	46,023
2009.08695	Searching for Low-Bit Weights in Quantized Neural Networks	Quantized neural networks with low-bit weights and activations are attractive for developing AI accelerators. However, the quantization functions used in most conventional quantization methods are non-differentiable, which increases the optimization difficulty of quantized networks. Compared with full-precision paramet...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	196,322
2212.06040	Semantic Decomposition Improves Learning of Large Language Models on EHR Data	Electronic health records (EHR) are widely believed to hold a profusion of actionable insights, encrypted in an irregular, semi-structured format, amidst a loud noise background. To simplify learning patterns of health and disease, medical codes in EHR can be decomposed into semantic units connected by hierarchical gra...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	335,988
2112.07859	Finite-Sample Analysis of Decentralized Q-Learning for Stochastic Games	Learning in stochastic games is arguably the most standard and fundamental setting in multi-agent reinforcement learning (MARL). In this paper, we consider decentralized MARL in stochastic games in the non-asymptotic regime. In particular, we establish the finite-sample complexity of fully decentralized Q-learning algo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	271,609
2111.06011	Climate Modeling with Neural Diffusion Equations	Owing to the remarkable development of deep learning technology, there have been a series of efforts to build deep learning-based climate models. Whereas most of them utilize recurrent neural networks and/or graph neural networks, we design a novel climate model based on the two concepts, the neural ordinary differenti...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	265,953
1404.3945	A Game Theoretic Approach to Minimize the Completion Time of Network Coded Cooperative Data Exchange	In this paper, we introduce a game theoretic framework for studying the problem of minimizing the completion time of instantly decodable network coding (IDNC) for cooperative data exchange (CDE) in decentralized wireless network. In this configuration, clients cooperate with each other to recover the erased packets wit...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	32,356
2002.08309	Simultaneous games with purchase of randomly supplied perfect information: Oracle Games	We study the role of costly information in non-cooperative two-player games when an extrinsic third party information broker is introduced asymmetrically, allowing one player to obtain information about the other player's action. This broker or "oracle" is defined by a probability of response, supplying correct informa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	164,714
2009.03793	Linear Temporal Public Announcement Logic: a new perspective for reasoning about the knowledge of multi-classifiers	In this note, a formal transition system model called LTPAL to extract knowledge in a classification process is suggested. The model combines the Public Announcement Logic (PAL) and the Linear Temporal Logic (LTL). In the model, first, we consider classifiers, which capture single-framed data. Next, we took classifiers...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	194,894
1802.07034	Memetic Graph Clustering	It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches. In this paper, we present a general memetic algorithm, VieClus, to tackle the graph clustering problem. This algorithm can be adapted to optimize different objective functions. A key com...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	90,815
1811.07619	Adversarial Soft-detection-based Aggregation Network for Image Retrieval	In recent year, the compact representations based on activations of Convolutional Neural Network (CNN) achieve remarkable performance in image retrieval. However, retrieval of some interested object that only takes up a small part of the whole image is still a challenging problem. Therefore, it is significant to extrac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,828
2202.00308	PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation	Despite their success, policy gradient methods suffer from high variance of the gradient estimate, which can result in unsatisfactory sample complexity. Recently, numerous variance-reduced extensions of policy gradient methods with provably better sample complexity and competitive numerical performance have been propos...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,101
1412.6586	A deep-structured fully-connected random field model for structured inference	There has been significant interest in the use of fully-connected graphical models and deep-structured graphical models for the purpose of structured inference. However, fully-connected and deep-structured graphical models have been largely explored independently, leaving the unification of these two concepts ripe for ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	38,665
1806.00292	Automatic Detection of Neurons in NeuN-stained Histological Images of Human Brain	In this paper, we present a novel use of an anisotropic diffusion model for automatic detection of neurons in histological sections of the adult human brain cortex. We use a partial differential equation model to process high resolution images to acquire locations of neuronal bodies. We also present a novel approach in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	99,280
2410.04546	Learning De-Biased Representations for Remote-Sensing Imagery	Remote sensing (RS) imagery, requiring specialized satellites to collect and being difficult to annotate, suffers from data scarcity and class imbalance in certain spectrums. Due to data scarcity, training any large-scale RS models from scratch is unrealistic, and the alternative is to transfer pre-trained models by fi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	495,339
2010.05466	Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching	Discriminatively localizing sounding objects in cocktail-party, i.e., mixed sound scenes, is commonplace for humans, but still challenging for machines. In this paper, we propose a two-stage learning framework to perform self-supervised class-aware sounding object localization. First, we propose to learn robust object ...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	200,144
2201.12950	Network Programming via Computable Products	The User Plane Function (UPF) aims to provide network services in the 3GPP 5G core network. These services need to be implemented on demand inexpensively with provable properties. Existing network dataplane programming languages are not up to the task. A new software paradigm is presented for the UPF. It is inspired by...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	277,845
2406.00262	Contrastive Learning Via Equivariant Representation	Invariant Contrastive Learning (ICL) methods have achieved impressive performance across various domains. However, the absence of latent space representation for distortion (augmentation)-related information in the latent space makes ICL sub-optimal regarding training efficiency and robustness in downstream tasks. Rece...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	459,770
2302.09395	When Visible-to-Thermal Facial GAN Beats Conditional Diffusion	Thermal facial imagery offers valuable insight into physiological states such as inflammation and stress by detecting emitted radiation in the infrared spectrum, which is unseen in the visible spectra. Telemedicine applications could benefit from thermal imagery, but conventional computers are reliant on RGB cameras an...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	346,407
2203.04041	Shape-invariant 3D Adversarial Point Clouds	Adversary and invisibility are two fundamental but conflict characters of adversarial perturbations. Previous adversarial attacks on 3D point cloud recognition have often been criticized for their noticeable point outliers, since they just involve an "implicit constrain" like global distance loss in the time-consuming ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	284,329
2005.00847	Sources of Transfer in Multilingual Named Entity Recognition	Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider polyglot named-entity recognition (NER), where one model is trained using annotated data drawn from more than one language. However, a straightforward implementation of this simple idea does n...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	175,403
2211.06959	mOKB6: A Multilingual Open Knowledge Base Completion Benchmark	Automated completion of open knowledge bases (Open KBs), which are constructed from triples of the form (subject phrase, relation phrase, object phrase), obtained via open information extraction (Open IE) system, are useful for discovering novel facts that may not be directly present in the text. However, research in O...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	330,080
2206.05014	Building an Icelandic Entity Linking Corpus	In this paper, we present the first Entity Linking corpus for Icelandic. We describe our approach of using a multilingual entity linking model (mGENRE) in combination with Wikipedia API Search (WAPIS) to label our data and compare it to an approach using WAPIS only. We find that our combined method reaches 53.9% covera...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	301,857
0910.1026	A multiagent urban traffic simulation. Part II: dealing with the extraordinary	In Probabilistic Risk Management, risk is characterized by two quantities: the magnitude (or severity) of the adverse consequences that can potentially result from the given activity or action, and by the likelihood of occurrence of the given adverse consequences. But a risk seldom exists in isolation: chain of consequ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	4,646
2012.13045	Regret Bound Balancing and Elimination for Model Selection in Bandits and RL	We propose a simple model selection approach for algorithms in stochastic bandit and reinforcement learning problems. As opposed to prior work that (implicitly) assumes knowledge of the optimal regret, we only require that each base algorithm comes with a candidate regret bound that may or may not hold during all round...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	213,092
2405.14728	Intervention and Conditioning in Causal Bayesian Networks	Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assume...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,566
1810.02054	Gradient Descent Provably Optimizes Over-parameterized Neural Networks	One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	109,525
2308.04733	TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design	Text design is one of the most critical procedures in poster design, as it relies heavily on the creativity and expertise of humans to design text images considering the visual harmony and text-semantic. This study introduces TextPainter, a novel multimodal approach that leverages contextual visual information and corr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,533
2307.07286	One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching	One-shot skeleton action recognition, which aims to learn a skeleton action recognition model with a single training sample, has attracted increasing interest due to the challenge of collecting and annotating large-scale skeleton action data. However, most existing studies match skeleton sequences by comparing their fe...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	379,346
1912.07936	Probabilistic Software Modeling: A Data-driven Paradigm for Software Analysis	Software systems are complex, and behavioral comprehension with the increasing amount of AI components challenges traditional testing and maintenance strategies.The lack of tools and methodologies for behavioral software comprehension leaves developers to testing and debugging that work in the boundaries of known scena...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	157,723
2102.02463	DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues	In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-value that are the same as the training data. In this study, a new deep...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	218,417
1406.2134	Rescue Robotics in Bore well Environment	A technique for rescue task in bore well environment has been proposed. India is facing a distressed cruel situation where in the previous years a number of child deaths have been reported falling in the bore well. As the diameter of the bore well is quiet narrow for any adult person and the lights goes dark inside it,...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	33,717
1701.01745	Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps	A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced. The proposed approach develops a hyperspectral-appropriate version of the SLIC superpixel segmentation algorithm, leverages map information to guide segmentation, and incorporates the semi-supervised Partial Membership La...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	66,446
2303.17559	DDP: Diffusion Model for Dense Visual Prediction	We propose a simple, efficient, yet powerful framework for dense visual predictions based on the conditional diffusion pipeline. Our approach follows a "noise-to-map" generative paradigm for prediction by progressively removing noise from a random Gaussian distribution, guided by the image. The method, called DDP, effi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	355,247
2102.11757	EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss	Maximum likelihood estimation is widely used in training Energy-based models (EBMs). Training requires samples from an unnormalized distribution, which is usually intractable, and in practice, these are obtained by MCMC algorithms such as Langevin dynamics. However, since MCMC in high-dimensional space converges extrem...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,516
2412.06706	Asynchronous Agents with Perfect Recall: Model Reductions, Knowledge-Based Construction, and Model Checking for Coalitional Strategies	Model checking of strategic abilities for agents with memory is a notoriously hard problem, and very few attempts have been made to tackle it. In this paper, we present two important steps towards this goal. First, we take the partial-order reduction scheme that was recently proved to preserve individual and coalitiona...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	515,340
2404.15472	Understanding Robot Minds: Leveraging Machine Teaching for Transparent Human-Robot Collaboration Across Diverse Groups	In this work, we aim to improve transparency and efficacy in human-robot collaboration by developing machine teaching algorithms suitable for groups with varied learning capabilities. While previous approaches focused on tailored approaches for teaching individuals, our method teaches teams with various compositions of...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	449,110
1911.04692	Equalization Loss for Large Vocabulary Instance Segmentation	Recent object detection and instance segmentation tasks mainly focus on datasets with a relatively small set of categories, e.g. Pascal VOC with 20 classes and COCO with 80 classes. The new large vocabulary dataset LVIS brings new challenges to conventional methods. In this work, we propose an equalization loss to solv...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	153,060
1909.08855	How Additional Knowledge can Improve Natural Language Commonsense Question Answering?	Recently several datasets have been proposed to encourage research in Question Answering domains where commonsense knowledge is expected to play an important role. Recent language models such as ROBERTA, BERT and GPT that have been pre-trained on Wikipedia articles and books have shown reasonable performance with littl...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	146,075
2201.01872	Gait Analysis for A Tilt-rotor: The Dynamic Invertible Gait	Conventional Feedback-Linearization-based controller, applied to the tilt-rotor (eight inputs), results in the extensive changes in the tilting angles, which are not expected in practice. To solve this problem, we introduce the novel concept UAV gait to restrict the tilting angles. The gait plan was initially to solve ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	274,383
1610.06052	Scheduling Broadcasts in a Network of Timelines	Broadcasts and timelines are the primary mechanism of information exchange in online social platforms today. Services like Facebook, Twitter and Instagram have enabled ordinary people to reach large audiences spanning cultures and countries, while their massive popularity has created increasingly competitive marketplac...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	62,598
2303.03542	Multi-resolution Interpretation and Diagnostics Tool for Natural Language Classifiers	Developing explainability methods for Natural Language Processing (NLP) models is a challenging task, for two main reasons. First, the high dimensionality of the data (large number of tokens) results in low coverage and in turn small contributions for the top tokens, compared to the overall model performance. Second, o...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	349,762
2202.02529	Graph Neural Network with Curriculum Learning for Imbalanced Node Classification	Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification. In this work, we reveal the vulnerability of GNN to the imbalance of node labels. Traditional solutions for imbalanced classification (e.g. resampling) are ineffective in node classification without consideri...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	278,849
1207.5409	FST Based Morphological Analyzer for Hindi Language	Hindi being a highly inflectional language, FST (Finite State Transducer) based approach is most efficient for developing a morphological analyzer for this language. The work presented in this paper uses the SFST (Stuttgart Finite State Transducer) tool for generating the FST. A lexicon of root words is created. Rules ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	17,718
2309.04250	Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems	Recommender systems, while transformative in online user experiences, have raised concerns over potential provider-side fairness issues. These systems may inadvertently favor popular items, thereby marginalizing less popular ones and compromising provider fairness. While previous research has recognized provider-side f...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	390,659
2303.10464	SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models	The pre-training and fine-tuning paradigm has contributed to a number of breakthroughs in Natural Language Processing (NLP). Instead of directly training on a downstream task, language models are first pre-trained on large datasets with cross-domain knowledge (e.g., Pile, MassiveText, etc.) and then fine-tuned on task-...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	352,465
1810.09776	Visual Semantic Re-ranker for Text Spotting	Many current state-of-the-art methods for text recognition are based on purely local information and ignore the semantic correlation between text and its surrounding visual context. In this paper, we propose a post-processing approach to improve the accuracy of text spotting by using the semantic relation between the t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,126
2206.01715	Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis	Randomized smoothing is the dominant standard for provable defenses against adversarial examples. Nevertheless, this method has recently been proven to suffer from important information theoretic limitations. In this paper, we argue that these limitations are not intrinsic, but merely a byproduct of current certificati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	300,572
1110.6317	Risk-sensitive Markov control processes	We introduce a general framework for measuring risk in the context of Markov control processes with risk maps on general Borel spaces that generalize known concepts of risk measures in mathematical finance, operations research and behavioral economics. Within the framework, applying weighted norm spaces to incorporate ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,809
2304.07453	Context-aware Domain Adaptation for Time Series Anomaly Detection	Time series anomaly detection is a challenging task with a wide range of real-world applications. Due to label sparsity, training a deep anomaly detector often relies on unsupervised approaches. Recent efforts have been devoted to time series domain adaptation to leverage knowledge from similar domains. However, existi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	358,349
1808.04456	Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction	Deep learning algorithms excel at extracting patterns from raw data, and with large datasets, they have been very successful in computer vision and natural language applications. However, in other domains, large datasets on which to learn representations from may not exist. In this work, we develop a novel multimodal C...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	105,153
2102.01788	Recurrent Neural Network for MoonBoard Climbing Route Classification and Generation	Classifying the difficulties of climbing routes and generating new routes are both challenging. Existing machine learning models not only fail to accurately predict a problem's difficulty, but they are also unable to generate reasonable problems. In this work, we introduced "BetaMove", a new move preprocessing pipeline...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	218,220
1809.10932	SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging	Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time. In this work, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	109,017
1803.02225	Subspace Tracking and Least Squares Approaches to Channel Estimation in Millimeter Wave Multiuser MIMO	The problem of MIMO channel estimation at millimeter wave frequencies, both in a single-user and in a multi-user setting, is tackled in this paper. Using a subspace approach, we develop a protocol enabling the estimation of the right (resp. left) singular vectors at the transmitter (resp. receiver) side; then, we adapt...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	92,019
2109.12951	Pragmatic competence of pre-trained language models through the lens of discourse connectives	As pre-trained language models (LMs) continue to dominate NLP, it is increasingly important that we understand the depth of language capabilities in these models. In this paper, we target pre-trained LMs' competence in pragmatics, with a focus on pragmatics relating to discourse connectives. We formulate cloze-style te...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	257,471
2309.15595	Advancing the distributed Multi-GPU ChASE library through algorithm optimization and NCCL library	As supercomputers become larger with powerful Graphics Processing Unit (GPU), traditional direct eigensolvers struggle to keep up with the hardware evolution and scale efficiently due to communication and synchronization demands. Conversely, subspace eigensolvers, like the Chebyshev Accelerated Subspace Eigensolver (Ch...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	395,032
2002.02819	Lightning Network: a second path towards centralisation of the Bitcoin economy	The Bitcoin Lightning Network (BLN), a so-called "second layer" payment protocol, was launched in 2018 to scale up the number of transactions between Bitcoin owners. In this paper, we analyse the structure of the BLN over a period of 18 months, ranging from 12th January 2018 to 17th July 2019. Here, we consider three r...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	163,041
2411.06076	BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges	This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. By leveraging both the capabilities of LLMs and Transformer-based models, this study evaluates BreakGPT and other Transformer-bas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,957
2001.06499	Temporal Interlacing Network	For a long time, the vision community tries to learn the spatio-temporal representation by combining convolutional neural network together with various temporal models, such as the families of Markov chain, optical flow, RNN and temporal convolution. However, these pipelines consume enormous computing resources due to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	160,811
1910.05810	Deep Crowd-Flow Prediction in Built Environments	Predicting the behavior of crowds in complex environments is a key requirement in a multitude of application areas, including crowd and disaster management, architectural design, and urban planning. Given a crowd's immediate state, current approaches simulate crowd movement to arrive at a future state. However, most ap...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	149,177
2104.10408	On User Interfaces for Large-Scale Document-Level Human Evaluation of Machine Translation Outputs	Recent studies emphasize the need of document context in human evaluation of machine translations, but little research has been done on the impact of user interfaces on annotator productivity and the reliability of assessments. In this work, we compare human assessment data from the last two WMT evaluation campaigns co...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	231,572
2409.08829	Community Fact-Checks Trigger Moral Outrage in Replies to Misleading Posts on Social Media	Displaying community fact-checks is a promising approach to reduce engagement with misinformation on social media. However, how users respond to misleading content emotionally after community fact-checks are displayed on posts is unclear. Here, we employ quasi-experimental methods to causally analyze changes in sentime...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	488,075
1808.02056	Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning	Cardiovascular disease accounts for 1 in every 4 deaths in United States. Accurate estimation of structural and functional cardiac parameters is crucial for both diagnosis and disease management. In this work, we develop an ensemble learning framework for more accurate and robust left ventricle (LV) quantification. The...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,697
2401.04367	Probabilistic emotion and sentiment modelling of patient-reported experiences	This study introduces a novel methodology for modelling patient emotions from online patient experience narratives. We employed metadata network topic modelling to analyse patient-reported experiences from Care Opinion, revealing key emotional themes linked to patient-caregiver interactions and clinical outcomes. We de...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	420,411
2208.07365	Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective	Unsupervised video domain adaptation is a practical yet challenging task. In this work, for the first time, we tackle it from a disentanglement view. Our key idea is to handle the spatial and temporal domain divergence separately through disentanglement. Specifically, we consider the generation of cross-domain videos f...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	313,030
2310.06176	Factual and Personalized Recommendations using Language Models and Reinforcement Learning	Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences. While traditional RSs rely on implicit user feedback signals, conversational RSs interact with users in natural language. In this work, we develop a comPell...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	398,455
2110.10366	Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training Examples	Convolutional Neural Networks (CNNs) for visual tasks are believed to learn both the low-level textures and high-level object attributes, throughout the network depth. This paper further investigates the `texture bias' in CNNs. To this end, we regenerate multiple instances of training examples from each original image,...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	262,128
2109.07165	3D Annotation Of Arbitrary Objects In The Wild	Recent years have produced a variety of learning based methods in the context of computer vision and robotics. Most of the recently proposed methods are based on deep learning, which require very large amounts of data compared to traditional methods. The performance of the deep learning methods are largely dependent on...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	255,416
1806.06984	Repetition Estimation	Visual repetition is ubiquitous in our world. It appears in human activity (sports, cooking), animal behavior (a bee's waggle dance), natural phenomena (leaves in the wind) and in urban environments (flashing lights). Estimating visual repetition from realistic video is challenging as periodic motion is rarely perfectl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,808
2106.07032	Category Theory in Machine Learning	Over the past two decades machine learning has permeated almost every realm of technology. At the same time, many researchers have begun using category theory as a unifying language, facilitating communication between different scientific disciplines. It is therefore unsurprising that there is a burgeoning interest in ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	240,740
1504.00680	Antisocial Behavior in Online Discussion Communities	User contributions in the form of posts, comments, and votes are essential to the success of online communities. However, allowing user participation also invites undesirable behavior such as trolling. In this paper, we characterize antisocial behavior in three large online discussion communities by analyzing users who...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	41,722
1511.06458	Bayesian inference via rejection filtering	We provide a method for approximating Bayesian inference using rejection sampling. We not only make the process efficient, but also dramatically reduce the memory required relative to conventional methods by combining rejection sampling with particle filtering. We also provide an approximate form of rejection sampling ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	49,262
2203.12738	Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing	Federated learning is a prime candidate for distributed machine learning at the network edge due to the low communication complexity and privacy protection among other attractive properties. However, existing algorithms face issues with slow convergence and/or robustness of performance due to the considerable heterogen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	287,374
1709.04744	Subspace Clustering using Ensembles of $K$-Subspaces	Subspace clustering is the unsupervised grouping of points lying near a union of low-dimensional linear subspaces. Algorithms based directly on geometric properties of such data tend to either provide poor empirical performance, lack theoretical guarantees, or depend heavily on their initialization. We present a novel ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	80,722
2207.00718	Triangle-oriented Community Detection considering Node Features and Network Topology	The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order det...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	305,858
2005.00419	Aggregation and Finetuning for Clothes Landmark Detection	Landmark detection for clothes is a fundamental problem for many applications. In this paper, a new training scheme for clothes landmark detection: $\textit{Aggregation and Finetuning}$, is proposed. We investigate the homogeneity among landmarks of different categories of clothes, and utilize it to design the procedur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	175,231
2206.00520	Deep Learning Opacity in Scientific Discovery	Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	300,159
2204.00949	Matching Feature Sets for Few-Shot Image Classification	In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classification methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	289,445
2203.09486	An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models	We propose a framework for training non-autoregressive sequence-to-sequence models for editing tasks, where the original input sequence is iteratively edited to produce the output. We show that the imitation learning algorithms designed to train such models for machine translation introduces mismatches between training...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	286,172
2108.01728	A Study on Herd Behavior Using Sentiment Analysis in Online Social Network	Social media platforms are thriving nowadays, so a huge volume of data is produced. As it includes brief and clear statements, millions of people post their thoughts on microblogging sites every day. This paper represents and analyze the capacity of diverse strategies to volumetric, delicate, and social networks to pre...	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	249,109
2311.03154	Convergence Analysis of Sequential Federated Learning on Heterogeneous Data	There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: i) parallel FL (PFL), where clients train models in a parallel manner; and ii) sequential FL (SFL), where clients train models in a sequential manner. In contrast to that of PFL, the convergence theory of SFL on h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	405,737
2209.02976	YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications	For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios. In this technical report, we strive to push its limits to the next level, stepping forwa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	316,359
1608.02996	Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders	Current approaches to learning vector representations of text that are compatible between different languages usually require some amount of parallel text, aligned at word, sentence or at least document level. We hypothesize however, that different natural languages share enough semantic structure that it should be pos...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	59,622
2308.00257	Trajectory Tracking via Multiscale Continuous Attractor Networks	Animals and insects showcase remarkably robust and adept navigational abilities, up to literally circumnavigating the globe. Primary progress in robotics inspired by these natural systems has occurred in two areas: highly theoretical computational neuroscience models, and handcrafted systems like RatSLAM and NeuroSLAM....	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	382,873
1905.01962	Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector	We investigate the recently developed Bidirectional Encoder Representations from Transformers (BERT) model for the hyperpartisan news detection task. Using a subset of hand-labeled articles from SemEval as a validation set, we test the performance of different parameters for BERT models. We find that accuracy from two ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	129,851
2502.10180	Safe platooning control of connected and autonomous vehicles on curved multi-lane roads	This paper investigates the safe platoon formation tracking and merging control problem of connected and automated vehicles (CAVs) on curved multi-lane roads. The first novelty is the separation of the control designs into two distinct parts: a lateral control law that ensures a geometrical convergence towards the refe...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	533,763
1903.08206	Aligning Biomedical Metadata with Ontologies Using Clustering and Embeddings	The metadata about scientific experiments published in online repositories have been shown to suffer from a high degree of representational heterogeneity---there are often many ways to represent the same type of information, such as a geographical location via its latitude and longitude. To harness the potential that m...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	124,786
2405.13870	FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept Composition	Benefiting from large-scale pre-trained text-to-image (T2I) generative models, impressive progress has been achieved in customized image generation, which aims to generate user-specified concepts. Existing approaches have extensively focused on single-concept customization and still encounter challenges when it comes t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	456,120
2303.10497	Examining the Potential for Conversational Exploratory Search using a Smart Speaker Digital Assistant	Online Digital Assistants, such as Amazon Alexa, Google Assistant, Apple Siri are very popular and provide a range or services to their users, a key function is their ability to satisfy user information needs from the sources available to them. Users may often regard these applications as providing search services simi...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	352,475
2204.08058	MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration	Multimodal video-audio-text understanding and generation can benefit from datasets that are narrow but rich. The narrowness allows bite-sized challenges that the research community can make progress on. The richness ensures we are making progress along the core challenges. To this end, we present a large-scale video-au...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	291,946
2003.05992	Comments on `Design and Implementation of Model-Predictive Control With Friction Compensation on an Omnidirectional Mobile Robot'	There are errors in the dynamics model in \cite{b1}. In addition, some details of the derivations and assumptions are missing in the paper. This letter was submitted to the IEEE Transactions on Mechatronics and although reviewers acknowledged the merits of the paper, but was not finally approved to be published and sug...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	167,995
2405.10913	Blackbox Adaptation for Medical Image Segmentation	In recent years, various large foundation models have been proposed for image segmentation. There models are often trained on large amounts of data corresponding to general computer vision tasks. Hence, these models do not perform well on medical data. There have been some attempts in the literature to perform paramete...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	454,926
1310.2473	Improved Decoding Algorithms for Reed-Solomon Codes	In coding theory, Reed-Solomon codes are one of the most well-known and widely used classes of error-correcting codes. In this thesis we study and compare two major strategies known for their decoding procedure, the Peterson-Gorenstein-Zierler (PGZ) and the Berlekamp-Massey (BM) decoder, in order to improve existing de...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	27,676

Subsets and Splits

No community queries yet

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