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
2306.04288	Revising deep learning methods in parking lot occupancy detection	Parking guidance systems have recently become a popular trend as a part of the smart cities' paradigm of development. The crucial part of such systems is the algorithm allowing drivers to search for available parking lots across regions of interest. The classic approach to this task is based on the application of neura...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	371,683
0803.1695	Use of self-correlation metrics for evaluation of information properties of binary strings	It is demonstrated that appropriately chosen computable metrics based on self-correlation properties provide a degree of determinism sufficient to segregate binary strings by level of information content.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,427
1907.12763	Finding Moments in Video Collections Using Natural Language	We introduce the task of retrieving relevant video moments from a large corpus of untrimmed, unsegmented videos given a natural language query. Our task poses unique challenges as a system must efficiently identify both the relevant videos and localize the relevant moments in the videos. To address these challenges, we...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	140,195
1901.01331	The ISTI Rapid Response on Exploring Cloud Computing 2018	This report describes eighteen projects that explored how commercial cloud computing services can be utilized for scientific computation at national laboratories. These demonstrations ranged from deploying proprietary software in a cloud environment to leveraging established cloud-based analytics workflows for processi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	117,947
1904.04989	FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking	Data association-based multiple object tracking (MOT) involves multiple separated modules processed or optimized differently, which results in complex method design and requires non-trivial tuning of parameters. In this paper, we present an end-to-end model, named FAMNet, where Feature extraction, Affinity estimation a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,175
2011.13230	Molecular representation learning with language models and domain-relevant auxiliary tasks	We apply a Transformer architecture, specifically BERT, to learn flexible and high quality molecular representations for drug discovery problems. We study the impact of using different combinations of self-supervised tasks for pre-training, and present our results for the established Virtual Screening and QSAR benchmar...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	208,420
1906.00278	Multi-dimensional Spectral Super-Resolution with Prior Knowledge via Frequency-Selective Vandermonde Decomposition and ADMM	This paper is concerned with estimation of multiple frequencies from incomplete and/or noisy samples based on a low-CP-rank tensor data model where each CP vector is an array response vector of one frequency. Suppose that it is known a priori that the frequencies lie in some given intervals, we develop efficient super-...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	133,335
2206.13317	Automatic identification of segmentation errors for radiotherapy using geometric learning	Automatic segmentation of organs-at-risk (OARs) in CT scans using convolutional neural networks (CNNs) is being introduced into the radiotherapy workflow. However, these segmentations still require manual editing and approval by clinicians prior to clinical use, which can be time consuming. The aim of this work was to ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	304,914
2010.14700	Sparse Symmetric Tensor Regression for Functional Connectivity Analysis	Tensor regression models, such as CP regression and Tucker regression, have many successful applications in neuroimaging analysis where the covariates are of ultrahigh dimensionality and possess complex spatial structures. The high-dimensional covariate arrays, also known as tensors, can be approximated by low-rank str...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	203,539
2308.14296	RecMind: Large Language Model Powered Agent For Recommendation	While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constra...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	388,265
2009.07354	Random-Sampling Monte-Carlo Tree Search Methods for Cost Approximation in Long-Horizon Optimal Control	In this paper, we develop Monte-Carlo based heuristic approaches to approximate the objective function in long horizon optimal control problems. In these approaches, to approximate the expectation operator in the objective function, we evolve the system state over multiple trajectories into the future while sampling th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	195,887
2003.11632	Pores for thought: The use of generative adversarial networks for the stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries	The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices. This work implements a deep convolutional generative adversarial network (DC-GAN) for generating realistic n-phase microstructural data. The same network architecture is succes...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	169,667
1711.01467	Attentional Pooling for Action Recognition	We introduce a simple yet surprisingly powerful model to incorporate attention in action recognition and human object interaction tasks. Our proposed attention module can be trained with or without extra supervision, and gives a sizable boost in accuracy while keeping the network size and computational cost nearly the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	83,890
1807.01488	Factored Bandits	We introduce the factored bandits model, which is a framework for learning with limited (bandit) feedback, where actions can be decomposed into a Cartesian product of atomic actions. Factored bandits incorporate rank-1 bandits as a special case, but significantly relax the assumptions on the form of the reward function...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	102,075
1906.00358	Data Augmentation for Object Detection via Progressive and Selective Instance-Switching	Collection of massive well-annotated samples is effective in improving object detection performance but is extremely laborious and costly. Instead of data collection and annotation, the recently proposed Cut-Paste methods [12, 15] show the potential to augment training dataset by cutting foreground objects and pasting ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	133,369
1812.00843	Early Prediction of Course Grades: Models and Feature Selection	In this paper, we compare predictive models for students' final performance in a blended course using a set of generic features collected from the first six weeks of class. These features were extracted from students' online homework submission logs as well as other online actions. We compare the effectiveness of 5 dif...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	115,358
2205.08719	$(O,G)$-granular variable precision fuzzy rough sets based on overlap and grouping functions	Since Bustince et al. introduced the concepts of overlap and grouping functions, these two types of aggregation functions have attracted a lot of interest in both theory and applications. In this paper, the depiction of $(O,G)$-granular variable precision fuzzy rough sets ($(O,G)$-GVPFRSs for short) is first given base...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	297,034
1409.6981	Unsupervised learning of regression mixture models with unknown number of components	Regression mixture models are widely studied in statistics, machine learning and data analysis. Fitting regression mixtures is challenging and is usually performed by maximum likelihood by using the expectation-maximization (EM) algorithm. However, it is well-known that the initialization is crucial for EM. If the init...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	36,283
1805.06474	Understanding Federation: An Analytical Framework for the Interoperability of Social Networking Sites	Although social networking has become a remarkable feature in the Web, full interoperability has not arrived. This work explores the main 5 paradigms of interoperability across social networking sites, corresponding to the layers in which we an find interoperability. Building on those, a novel analytical framework for ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	97,609
2308.08290	DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning	To address the communication burden issues associated with federated learning (FL), decentralized federated learning (DFL) discards the central server and establishes a decentralized communication network, where each client communicates only with neighboring clients. However, existing DFL methods still suffer from two ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	385,846
2304.13517	Leveraging Compositional Methods for Modeling and Verification of an Autonomous Taxi System	We apply a compositional formal modeling and verification method to an autonomous aircraft taxi system. We provide insights into the modeling approach and we identify several research areas where further development is needed. Specifically, we identify the following needs: (1) semantics of composition of viewpoints exp...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	360,606
2303.10454	Performance Analysis and Optimization of Multi-RIS-Aided UAV Networks	In this paper, we study the performance of multiple reconfigurable intelligent surfaces (RISs)-aided unmanned aerial vehicle (UAV) communication networks over Nakagami-$m$ fading channels. For that purpose, we used accurate closed-form approximations for the channel distributions to derive closed-form approximations fo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	352,459
1909.02667	Bandwidth Embeddings for Mixed-bandwidth Speech Recognition	In this paper, we tackle the problem of handling narrowband and wideband speech by building a single acoustic model (AM), also called mixed bandwidth AM. In the proposed approach, an auxiliary input feature is used to provide the bandwidth information to the model, and bandwidth embeddings are jointly learned as part o...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	144,252
2307.00411	Applications of Binary Similarity and Distance Measures	In the recent past, binary similarity measures have been applied in solving biometric identification problems, including fingerprint, handwritten character detection, and in iris image recognition. The application of the relevant measurements has also resulted in more accurate data analysis. This paper surveys the appl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	376,996
2004.02579	Best Linear Approximation of Nonlinear Continuous-Time Systems Subject to Process Noise and Operating in Feedback	In many engineering applications the level of nonlinear distortions in frequency response function (FRF) measurements is quantified using specially designed periodic excitation signals called random phase multisines and periodic noise. The technique is based on the concept of the best linear approximation (BLA) and it ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	171,269
2304.13732	Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehicles	Accurately detecting and predicting lane change (LC)processes of human-driven vehicles can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety. This paper focuses on LC processes, first developing a temporal convolutional network with ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	360,689
1111.1048	Achievable and Crystallized Rate Regions of the Interference Channel with Interference as Noise	The interference channel achievable rate region is presented when the interference is treated as noise. The formulation starts with the 2-user channel, and then extends the results to the n-user case. The rate region is found to be the convex hull of the union of n power control rate regions, where each power control r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	12,906
2312.16724	A pipeline for multiple orange detection and tracking with 3-D fruit relocalization and neural-net based yield regression in commercial citrus orchards	Traditionally, sweet orange crop forecasting has involved manually counting fruits from numerous trees, which is a labor-intensive process. Automatic systems for fruit counting, based on proximal imaging, computer vision, and machine learning, have been considered a promising alternative or complement to manual countin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,484
1704.06836	Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation	Sarcasm is a form of speech in which speakers say the opposite of what they truly mean in order to convey a strong sentiment. In other words, "Sarcasm is the giant chasm between what I say, and the person who doesn't get it.". In this paper we present the novel task of sarcasm interpretation, defined as the generation ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	72,229
2407.11986	Symbiotic Connectivity: Optimizing Rural Digital Infrastructure with Solar-Powered Mesh Networks Using Multi-Objective Evolutionary Algorithms	I present an open-source, ecologically integrated model for rural connectivity, merging the location of nodes mesh networks with renewable energy systems. Employing evolutionary algorithms, this approach optimizes node placement for internet access and symbiotic energy distribution. This model, grounded in community co...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	473,705
2108.12802	Interpretable Propaganda Detection in News Articles	Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis. To counter thus, a number of approaches have been designed aiming to achieve a healthier and safer online news and media consumption. Automatic systems are able to support humans in detecting such content; ye...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	252,612
1703.06109	Generalised Reichenbachian Common Cause Systems	The principle of the common cause claims that if an improbable coincidence has occurred, there must exist a common cause. This is generally taken to mean that positive correlations between non-causally related events should disappear when conditioning on the action of some underlying common cause. The extended interpre...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	70,177
1711.08200	Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification	The work in this paper is driven by the question how to exploit the temporal cues available in videos for their accurate classification, and for human action recognition in particular? Thus far, the vision community has focused on spatio-temporal approaches with fixed temporal convolution kernel depths. We introduce a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,157
1710.01559	Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks	This paper investigates the automatic monitoring of tool usage during a surgery, with potential applications in report generation, surgical training and real-time decision support. Two surgeries are considered: cataract surgery, the most common surgical procedure, and cholecystectomy, one of the most common digestive s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	82,034
1610.07086	Deep image mining for diabetic retinopathy screening	Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert knowledge about the target pathologies. However, deep learning algori...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	62,736
2308.02374	Optimal Sizing of On-site Renewable Resources for Offshore Microgrids	The offshore oil and natural gas platforms, mostly powered by diesel or gas generators, consume approximately 16TWh of electricity worldwide per year, which emits large amount of CO2. To limit their contribution to climate change, a proposed solution is to replace the traditional fossil fuel based energy resources with...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	383,596
2009.13291	Physics Informed Neural Networks for Simulating Radiative Transfer	We propose a novel machine learning algorithm for simulating radiative transfer. Our algorithm is based on physics informed neural networks (PINNs), which are trained by minimizing the residual of the underlying radiative tranfer equations. We present extensive experiments and theoretical error estimates to demonstrate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,700
1910.03268	Integrated Optimization of Ascent Trajectory and SRM Design of Multistage Launch Vehicles	This paper presents a methodology for the concurrent first-stage preliminary design and ascent trajectory optimization, with application to a Vega-derived Light Launch Vehicle. The reuse as first stage of an existing upper-stage (Zefiro 40) requires a propellant grain geometry redesign, in order to account for the muta...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	148,461
1805.11897	Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance	Applications of optimal transport have recently gained remarkable attention thanks to the computational advantages of entropic regularization. However, in most situations the Sinkhorn approximation of the Wasserstein distance is replaced by a regularized version that is less accurate but easy to differentiate. In this ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	99,045
2012.12474	Self-supervised self-supervision by combining deep learning and probabilistic logic	Labeling training examples at scale is a perennial challenge in machine learning. Self-supervision methods compensate for the lack of direct supervision by leveraging prior knowledge to automatically generate noisy labeled examples. Deep probabilistic logic (DPL) is a unifying framework for self-supervised learning tha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	212,948
1907.08461	Delegative Reinforcement Learning: learning to avoid traps with a little help	Most known regret bounds for reinforcement learning are either episodic or assume an environment without traps. We derive a regret bound without making either assumption, by allowing the algorithm to occasionally delegate an action to an external advisor. We thus arrive at a setting of active one-shot model-based reinf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	139,108
1907.07677	CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation	This paper proposes a novel cascaded U-Net for brain tumor segmentation. Inspired by the distinct hierarchical structure of brain tumor, we design a cascaded deep network framework, in which the whole tumor is segmented firstly and then the tumor internal substructures are further segmented. Considering that the increa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	138,939
2409.13280	Efficient Training of Deep Neural Operator Networks via Randomized Sampling	Neural operators (NOs) employ deep neural networks to learn mappings between infinite-dimensional function spaces. Deep operator network (DeepONet), a popular NO architecture, has demonstrated success in the real-time prediction of complex dynamics across various scientific and engineering applications. In this work, w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	489,924
2209.12062	Compressing bipartite graphs with a dual reordering scheme	In order to manage massive graphs in practice, it is often necessary to resort to graph compression, which aims at reducing the memory used when storing and processing the graph. Efficient compression methods have been proposed in the literature, especially for web graphs. In most cases, they are combined with a vertex...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	319,401
2002.03501	Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data	Segmentation of unseen industrial parts is essential for autonomous industrial systems. However, industrial components are texture-less, reflective, and often found in cluttered and unstructured environments with heavy occlusion, which makes it more challenging to deal with unseen objects. To tackle this problem, we pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	163,285
1507.04024	Analyzing the activity of a person in a chat by combining network analysis and fuzzy logic	Chat-log data that contains information about sender and receiver of the statements sent around in the chat can be readily turned into a directed temporal multi-network representation. In the resulting network, the activity of a chat member can, for example, be operationalized as his degree (number of distinct interact...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	45,126
2403.13870	ExMap: Leveraging Explainability Heatmaps for Unsupervised Group Robustness to Spurious Correlations	Group robustness strategies aim to mitigate learned biases in deep learning models that arise from spurious correlations present in their training datasets. However, most existing methods rely on the access to the label distribution of the groups, which is time-consuming and expensive to obtain. As a result, unsupervis...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	439,832
2107.03387	Sleep syndromes onset detection based on automatic sleep staging algorithm	In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes, including restless leg syndrome, insomnia, based on an algorithm that is comprised of two modules. A Fast Fourier Transform is applied to 30 seconds long epochs of EEG recordings to provide localized time-fr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	245,147
2201.02136	A fixed storage distributed graph database hybrid with at-scale OLAP expression and I/O support of a relational DB: Kinetica-Graph	A distributed graph database architecture that co-exists with the distributed relational DB for I/O and at-scale OLAP expression support with hundreds of PostGIS compatible geometry functions will be discussed in this article. The uniqueness of this implementation stems mainly from its double link topology structure fo...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	274,461
2012.07110	Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks	Nowadays, organizations collect vast quantities of sensitive information in `Enterprise Resource Planning' (ERP) systems, such as accounting relevant transactions, customer master data, or strategic sales price information. The leakage of such information poses a severe threat for companies as the number of incidents a...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	211,345
2405.07465	Deception in Differential Games: Information Limiting Strategy to Induce Dilemma	Can deception exist in differential games? We provide a case study for a Turret-Attacker differential game, where two Attackers seek to score points by reaching a target region while a Turret tries to minimize the score by aligning itself with the Attackers before they reach the target. In contrast to the original prob...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	453,723
2410.22283	Leveraging Recurrent Neural Networks for Predicting Motor Movements from Primate Motor Cortex Neural Recordings	This paper presents an efficient deep learning solution for decoding motor movements from neural recordings in non-human primates. An Autoencoder Gated Recurrent Unit (AEGRU) model was adopted as the model architecture for this task. The autoencoder is only used during the training stage to achieve better generalizatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	503,565
2304.08177	Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca	Large Language Models (LLMs), such as ChatGPT and GPT-4, have dramatically transformed natural language processing research and shown promising strides towards Artificial General Intelligence (AGI). Nonetheless, the high costs associated with training and deploying LLMs present substantial obstacles to transparent, acc...	true	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	358,617
1505.04094	Evaluating Link Prediction Methods	Link prediction is a popular research area with important applications in a variety of disciplines, including biology, social science, security, and medicine. The fundamental requirement of link prediction is the accurate and effective prediction of new links in networks. While there are many different methods proposed...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	false	43,144
2010.10833	KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision	Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions and limited in size, which makes supervised methods hard to detect causal relations between events. To...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	202,019
1905.06159	Deep Neural Architecture Search with Deep Graph Bayesian Optimization	Bayesian optimization (BO) is an effective method of finding the global optima of black-box functions. Recently BO has been applied to neural architecture search and shows better performance than pure evolutionary strategies. All these methods adopt Gaussian processes (GPs) as surrogate function, with the handcraft sim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	130,912
2302.02343	LExecutor: Learning-Guided Execution	Executing code is essential for various program analysis tasks, e.g., to detect bugs that manifest through exceptions or to obtain execution traces for further dynamic analysis. However, executing an arbitrary piece of code is often difficult in practice, e.g., because of missing variable definitions, missing user inpu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	343,967
1803.01719	How to Start Training: The Effect of Initialization and Architecture	We identify and study two common failure modes for early training in deep ReLU nets. For each we give a rigorous proof of when it occurs and how to avoid it, for fully connected and residual architectures. The first failure mode, exploding/vanishing mean activation length, can be avoided by initializing weights from a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,932
2305.16023	NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts	We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains. Previous CGEC research primarily focuses on correcting texts from a single domain, especially learner essays. To broaden the target domain, we annotate multiple refere...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	367,886
1910.10818	Approximate Stochastic Reachability for High Dimensional Systems	We present a method to compute the stochastic reachability safety probabilities for high-dimensional stochastic dynamical systems. Our approach takes advantage of a nonparametric learning technique known as conditional distribution embeddings to model the stochastic kernel using a data-driven approach. By embedding the...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	150,590
1907.07676	Lung Nodules Detection and Segmentation Using 3D Mask-RCNN	Accurate assessment of Lung nodules is a time consuming and error prone ingredient of the radiologist interpretation work. Automating 3D volume detection and segmentation can improve workflow as well as patient care. Previous works have focused either on detecting lung nodules from a full CT scan or on segmenting them ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	138,938
2312.07169	Semi-supervised Active Learning for Video Action Detection	In this work, we focus on label efficient learning for video action detection. We develop a novel semi-supervised active learning approach which utilizes both labeled as well as unlabeled data along with informative sample selection for action detection. Video action detection requires spatio-temporal localization alon...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,823
2008.10418	INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs	We consider the problem of integrating non-imaging information into segmentation networks to improve performance. Conditioning layers such as FiLM provide the means to selectively amplify or suppress the contribution of different feature maps in a linear fashion. However, spatial dependency is difficult to learn within...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,989
2010.12640	Avoiding Occupancy Detection from Smart Meter using Adversarial Machine Learning	More and more conventional electromechanical meters are being replaced with smart meters because of their substantial benefits such as providing faster bi-directional communication between utility services and end users, enabling direct load control for demand response, energy saving, and so on. However, the fine-grain...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	202,775
1706.01671	Compression Fractures Detection on CT	The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. Less than one third of vertebral compression fractures are diagnosed clinically. We present an automated meth...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,844
1705.00687	Convex-constrained Sparse Additive Modeling and Its Extensions	Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. In this work we show how shape constraints such as convexity/concavity and their extensions, can be integrated into additive models. The proposed sparse difference of convex additive models (SDCAM) can est...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	72,730
2107.09428	Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models	Non-autoregressive (NAR) modeling has gained more and more attention in speech processing. With recent state-of-the-art attention-based automatic speech recognition (ASR) structure, NAR can realize promising real-time factor (RTF) improvement with only small degradation of accuracy compared to the autoregressive (AR) m...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,027
2210.15621	Class Based Thresholding in Early Exit Semantic Segmentation Networks	We propose Class Based Thresholding (CBT) to reduce the computational cost of early exit semantic segmentation models while preserving the mean intersection over union (mIoU) performance. A key idea of CBT is to exploit the naturally-occurring neural collapse phenomenon. Specifically, by calculating the mean prediction...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	327,024
1905.06576	STAR: A Concise Deep Learning Framework for Citywide Human Mobility Prediction	Human mobility forecasting in a city is of utmost importance to transportation and public safety, but with the process of urbanization and the generation of big data, intensive computing and determination of mobility pattern have become challenging. This study focuses on how to improve the accuracy and efficiency of pr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	131,033
1306.1462	K-Algorithm A Modified Technique for Noise Removal in Handwritten Documents	OCR has been an active research area since last few decades. OCR performs the recognition of the text in the scanned document image and converts it into editable form. The OCR process can have several stages like pre-processing, segmentation, recognition and post processing. The pre-processing stage is a crucial stage ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	25,048
1909.13843	A Darwin Time Domain Scheme for the Simulation of Transient Quasistatic Electromagnetic Fields Including Resistive, Capacitive and Inductive Effects	The Darwin field model addresses an approximation to Maxwell's equations where radiation effects are neglected. It allows to describe general quasistatic electromagnetic field phenomena including inductive, resistive and capacitive effects. A Darwin formulation based on the Darwin-Amp\`ere equation and the implicitly i...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	147,532
1205.4683	How women organize social networks different from men	Superpositions of social networks, such as communication, friendship, or trade networks, are called multiplex networks, forming the structural backbone of human societies. Novel datasets now allow quantification and exploration of multiplex networks. Here we study gender-specific differences of a multiplex network from...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	16,113
2008.08178	Discovering Multi-Hardware Mobile Models via Architecture Search	Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored. In this paper, we argue that, for applications that may be deployed on m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,339
2408.11557	A Quick, trustworthy spectral knowledge Q&A system leveraging retrieval-augmented generation on LLM	Large Language Model (LLM) has demonstrated significant success in a range of natural language processing (NLP) tasks within general domain. The emergence of LLM has introduced innovative methodologies across diverse fields, including the natural sciences. Researchers aim to implement automated, concurrent process driv...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	482,342
1503.08155	Learning Embedding Representations for Knowledge Inference on Imperfect and Incomplete Repositories	This paper considers the problem of knowledge inference on large-scale imperfect repositories with incomplete coverage by means of embedding entities and relations at the first attempt. We propose IIKE (Imperfect and Incomplete Knowledge Embedding), a probabilistic model which measures the probability of each belief, i...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	41,553
2309.12028	Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting	This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past. The problem is typically solved by modeling complex spatio-temporal correlations in traffic data using spatio-temporal graph neural networks (GNNs...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	393,637
1604.02930	Implementation of haptic communication in comanipulative tasks: a statistical state machine model	- This paper presents an experimental evaluation of physical human-human interaction in lightweight condition using a one degree of freedom robotized setup. It explores possible origins of Physical Human-Human communication, more precisely, the hypothesis of a time based communication. To explore if the communication i...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	54,411
2412.06248	Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data	Growing privacy concerns and regulations like GDPR and CCPA necessitate pseudonymization techniques that protect identity in image datasets. However, retaining utility is also essential. Traditional methods like masking and blurring degrade quality and obscure critical context, especially in human-centric images. We in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,173
1903.03099	Lifted Weight Learning of Markov Logic Networks Revisited	We study lifted weight learning of Markov logic networks. We show that there is an algorithm for maximum-likelihood learning of 2-variable Markov logic networks which runs in time polynomial in the domain size. Our results are based on existing lifted-inference algorithms and recent algorithmic results on computing max...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	123,639
2112.12438	Using Sequential Statistical Tests for Efficient Hyperparameter Tuning	Hyperparameter tuning is one of the the most time-consuming parts in machine learning. Despite the existence of modern optimization algorithms that minimize the number of evaluations needed, evaluations of a single setting may still be expensive. Usually a resampling technique is used, where the machine learning method...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	272,960
1111.0059	Loosely Coupled Formulations for Automated Planning: An Integer Programming Perspective	We represent planning as a set of loosely coupled network flow problems, where each network corresponds to one of the state variables in the planning domain. The network nodes correspond to the state variable values and the network arcs correspond to the value transitions. The planning problem is to find a path (a sequ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	12,853
2004.14593	A Triangular Network For Density Estimation	We report a triangular neural network implementation of neural autoregressive flow (NAF). Unlike many universal autoregressive density models, our design is highly modular, parameter economy, computationally efficient, and applicable to density estimation of data with high dimensions. It achieves state-of-the-art bits-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	174,947
2012.10490	Perception-Based Temporal Logic Planning in Uncertain Semantic Maps	This paper addresses a multi-robot planning problem in environments with partially unknown semantics. The environment is assumed to have known geometric structure (e.g., walls) and to be occupied by static labeled landmarks with uncertain positions and classes. This modeling approach gives rise to an uncertain semantic...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	212,355
1805.08180	Hierarchical Reinforcement Learning with Hindsight	Reinforcement Learning (RL) algorithms can suffer from poor sample efficiency when rewards are delayed and sparse. We introduce a solution that enables agents to learn temporally extended actions at multiple levels of abstraction in a sample efficient and automated fashion. Our approach combines universal value functio...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	true	false	false	98,068
2002.02712	Discovering Mathematical Objects of Interest -- A Study of Mathematical Notations	Mathematical notation, i.e., the writing system used to communicate concepts in mathematics, encodes valuable information for a variety of information search and retrieval systems. Yet, mathematical notations remain mostly unutilized by today's systems. In this paper, we present the first in-depth study on the distribu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	163,007
2411.01121	Hedging and Pricing Structured Products Featuring Multiple Underlying Assets	Hedging a portfolio containing autocallable notes presents unique challenges due to the complex risk profile of these financial instruments. In addition to hedging, pricing these notes, particularly when multiple underlying assets are involved, adds another layer of complexity. Pricing autocallable notes involves intri...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	504,919
1806.04819	Integral Privacy for Sampling	Differential privacy is a leading protection setting, focused by design on individual privacy. Many applications, in medical / pharmaceutical domains or social networks, rather posit privacy at a group level, a setting we call integral privacy. We aim for the strongest form of privacy: the group size is in particular n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	100,323
1705.04763	High-Precision Trajectory Tracking in Changing Environments Through $\mathcal{L}_1$ Adaptive Feedback and Iterative Learning	As robots and other automated systems are introduced to unknown and dynamic environments, robust and adaptive control strategies are required to cope with disturbances, unmodeled dynamics and parametric uncertainties. In this paper, we propose and provide theoretical proofs of a combined $\mathcal{L}_1$ adaptive feedba...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	73,377
1805.05081	Constructing Narrative Event Evolutionary Graph for Script Event Prediction	Script event prediction requires a model to predict the subsequent event given an existing event context. Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. To remedy this, we propose constructing an event graph to ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	97,373
1705.09425	Hierarchical Cellular Automata for Visual Saliency	Saliency detection, finding the most important parts of an image, has become increasingly popular in computer vision. In this paper, we introduce Hierarchical Cellular Automata (HCA) -- a temporally evolving model to intelligently detect salient objects. HCA consists of two main components: Single-layer Cellular Automa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,195
2409.06366	One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion	Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is still missing a single learning framework that can control all these different embodiments easily and ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	487,102
2410.21053	Computable Lipschitz Bounds for Deep Neural Networks	Deriving sharp and computable upper bounds of the Lipschitz constant of deep neural networks is crucial to formally guarantee the robustness of neural-network based models. We analyse three existing upper bounds written for the $l^2$ norm. We highlight the importance of working with the $l^1$ and $l^\infty$ norms and w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	503,071
2403.00794	Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models	Humor is a fundamental facet of human cognition and interaction. Yet, despite recent advances in natural language processing, humor detection remains a challenging task that is complicated by the scarcity of datasets that pair humorous texts with similar non-humorous counterparts. In our work, we investigate whether la...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	434,106
1403.1486	Lifespan and propagation of information in On-line Social Networks a Case Study	Since 1950, information flows have been in the centre of scientific research. Up until internet penetration in the late 90s, these studies were based over traditional offline social networks. Several observations in offline information flows studies, such as two-step flow of communication and the importance of weak tie...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	31,399
2303.09950	Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration	We study the problem of outlier correspondence pruning for non-rigid point cloud registration. In rigid registration, spatial consistency has been a commonly used criterion to discriminate outliers from inliers. It measures the compatibility of two correspondences by the discrepancy between the respective distances in ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,252
1809.02292	A Block Coordinate Ascent Algorithm for Mean-Variance Optimization	Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare. The mean-variance function is one of the most widely used objective functions in risk management due to its simplicity and interpretability. Existing algorithms for mean-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	107,020
2404.09631	Action Model Learning with Guarantees	This paper studies the problem of action model learning with full observability. Following the learning by search paradigm by Mitchell, we develop a theory for action model learning based on version spaces that interprets the task as search for hypothesis that are consistent with the learning examples. Our theoretical ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	446,769
2311.16093	Visual cognition in multimodal large language models	A chief goal of artificial intelligence is to build machines that think like people. Yet it has been argued that deep neural network architectures fail to accomplish this. Researchers have asserted these models' limitations in the domains of causal reasoning, intuitive physics, and intuitive psychology. Yet recent adva...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	410,750
2103.11367	ROSITA: Refined BERT cOmpreSsion with InTegrAted techniques	Pre-trained language models of the BERT family have defined the state-of-the-arts in a wide range of NLP tasks. However, the performance of BERT-based models is mainly driven by the enormous amount of parameters, which hinders their application to resource-limited scenarios. Faced with this problem, recent studies have...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	225,783
2412.06181	Enhancing Adversarial Resistance in LLMs with Recursion	The increasing integration of Large Language Models (LLMs) into society necessitates robust defenses against vulnerabilities from jailbreaking and adversarial prompts. This project proposes a recursive framework for enhancing the resistance of LLMs to manipulation through the use of prompt simplification techniques. By...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	515,134

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