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
2312.00869	Segment and Caption Anything	We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions. SAM presents strong generalizability to segment anything while is short for semantic understanding. By introducing a lightweight query-based feature mixer, we align the region-specific features with...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,219
2307.06951	AI For Global Climate Cooperation 2023 Competition Proceedings	The international community must collaborate to mitigate climate change and sustain economic growth. However, collaboration is hard to achieve, partly because no global authority can ensure compliance with international climate agreements. Combining AI with climate-economic simulations offers a promising solution to de...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	379,231
2406.06658	Link Prediction in Bipartite Networks	Bipartite networks serve as highly suitable models to represent systems involving interactions between two distinct types of entities, such as online dating platforms, job search services, or ecommerce websites. These models can be leveraged to tackle a number of tasks, including link prediction among the most useful o...	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	false	462,729
2204.03067	ByT5 model for massively multilingual grapheme-to-phoneme conversion	In this study, we tackle massively multilingual grapheme-to-phoneme conversion through implementing G2P models based on ByT5. We have curated a G2P dataset from various sources that covers around 100 languages and trained large-scale multilingual G2P models based on ByT5. We found that ByT5 operating on byte-level inpu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	290,176
1903.10675	Document Similarity for Texts of Varying Lengths via Hidden Topics	Measuring similarity between texts is an important task for several applications. Available approaches to measure document similarity are inadequate for document pairs that have non-comparable lengths, such as a long document and its summary. This is because of the lexical, contextual and the abstraction gaps between a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	125,336
2211.13227	Paint by Example: Exemplar-based Image Editing with Diffusion Models	Language-guided image editing has achieved great success recently. In this paper, for the first time, we investigate exemplar-guided image editing for more precise control. We achieve this goal by leveraging self-supervised training to disentangle and re-organize the source image and the exemplar. However, the naive ap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	332,396
2405.15217	NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation	The success of denoising diffusion models in representing rich data distributions over 2D raster images has prompted research on extending them to other data representations, such as vector graphics. Unfortunately due to their variable structure and scarcity of vector training data, directly applying diffusion models o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	456,811
1906.05591	Finite Sample Analysis Of Dynamic Regression Parameter Learning	We consider the dynamic linear regression problem, where the predictor vector may vary with time. This problem can be modeled as a linear dynamical system, with non-constant observation operator, where the parameters that need to be learned are the variance of both the process noise and the observation noise. While var...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	135,067
2407.06322	MagMax: Leveraging Model Merging for Seamless Continual Learning	This paper introduces a continual learning approach named MagMax, which utilizes model merging to enable large pre-trained models to continuously learn from new data without forgetting previously acquired knowledge. Distinct from traditional continual learning methods that aim to reduce forgetting during task training,...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	471,356
2101.10001	Diverse Adversaries for Mitigating Bias in Training	Adversarial learning can learn fairer and less biased models of language than standard methods. However, current adversarial techniques only partially mitigate model bias, added to which their training procedures are often unstable. In this paper, we propose a novel approach to adversarial learning based on the use of ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	216,788
1604.08263	Market-based vs. Price-based Microgrid Optimal Scheduling	An optimal scheduling model for a microgrid participating in the electricity distribution market in interaction with a Distribution Market Operator (DMO) is proposed in this paper. The DMO administers the established electricity market in the distribution level, sets electricity prices, determines the amount of the pow...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	55,192
2408.04667	LLM Stability: A detailed analysis with some surprises	LLM (large language model) practitioners commonly notice that outputs can vary for the same inputs, but we have been unable to find work that evaluates LLM stability as the main objective. In our study of 6 deterministically configured LLMs across 8 common tasks with 5 identical runs, we see accuracy variations up to 1...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	479,480
1906.06968	Scrubbing Sensitive PHI Data from Medical Records made Easy by SpaCy -- A Scalable Model Implementation Comparisons	De-identification of clinical records is an extremely important process which enables the use of the wealth of information present in them. There are a lot of techniques available for this but none of the method implementation has evaluated the scalability, which is an important benchmark. We evaluated numerous deep le...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	135,475
1602.07860	Probably Approximately Correct Greedy Maximization with Efficient Bounds on Information Gain for Sensor Selection	Submodular function maximization finds application in a variety of real-world decision-making problems. However, most existing methods, based on greedy maximization, assume it is computationally feasible to evaluate F, the function being maximized. Unfortunately, in many realistic settings F is too expensive to evaluat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	52,573
2309.01933	Provably safe systems: the only path to controllable AGI	We describe a path to humanity safely thriving with powerful Artificial General Intelligences (AGIs) by building them to provably satisfy human-specified requirements. We argue that this will soon be technically feasible using advanced AI for formal verification and mechanistic interpretability. We further argue that i...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	389,855
2309.14211	QuadricsNet: Learning Concise Representation for Geometric Primitives in Point Clouds	This paper presents a novel framework to learn a concise geometric primitive representation for 3D point clouds. Different from representing each type of primitive individually, we focus on the challenging problem of how to achieve a concise and uniform representation robustly. We employ quadrics to represent diverse p...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	394,503
1702.02719	Effective face landmark localization via single deep network	In this paper, we propose a novel face alignment method using single deep network (SDN) on existing limited training data. Rather than using a max-pooling layer followed one convolutional layer in typical convolutional neural networks (CNN), SDN adopts a stack of 3 layer groups instead. Each group layer contains two co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,025
2410.15669	Learning to Generate and Evaluate Fact-checking Explanations with Transformers	In an era increasingly dominated by digital platforms, the spread of misinformation poses a significant challenge, highlighting the need for solutions capable of assessing information veracity. Our research contributes to the field of Explainable Artificial Antelligence (XAI) by developing transformer-based fact-checki...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	500,670
2408.08881	Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation	Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model, U-MedSAM, which integrates the MedSAM model with an uncertainty-aware...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	481,190
2306.01102	LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization	Large Language Models (LLMs) have emerged as powerful tools capable of accomplishing a broad spectrum of tasks. Their abilities span numerous areas, and one area where they have made a significant impact is in the domain of code generation. Here, we propose using the coding abilities of LLMs to introduce meaningful var...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	false	370,292
2309.11925	Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality Estimation Shared Task	We present the joint contribution of Unbabel and Instituto Superior T\'ecnico to the WMT 2023 Shared Task on Quality Estimation (QE). Our team participated on all tasks: sentence- and word-level quality prediction (task 1) and fine-grained error span detection (task 2). For all tasks, we build on the COMETKIWI-22 model...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	393,593
1910.11792	JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments	We present JRDB, a novel egocentric dataset collected from our social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360$^\circ$ RGB video at 15 fps, 3D point clouds from two Velodyne 16 Lidars, line 3D point clouds from two Sick Lidars, a...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	150,884
2203.04444	Reproducible Subjective Evaluation	Human perceptual studies are the gold standard for the evaluation of many research tasks in machine learning, linguistics, and psychology. However, these studies require significant time and cost to perform. As a result, many researchers use objective measures that can correlate poorly with human evaluation. When subje...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	284,472
2407.02172	RETINA: a hardware-in-the-loop optical facility with reduced optical aberrations	The increasing interest in spacecraft autonomy and the complex tasks to be accomplished by the spacecraft raise the need for a trustworthy approach to perform Verification & Validation of Guidance, Navigation, and Control algorithms. In the context of autonomous operations, vision-based navigation algorithms have estab...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	469,612
2207.05468	Sliced-Wasserstein normalizing flows: beyond maximum likelihood training	Despite their advantages, normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data (e.g., images) and their failing to detect out-of-distribution data. One reason for these deficiencies lies in the training strategy which traditionally exploits a maximum likelih...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	307,548
2412.18222	Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications	With the development of the financial industry, credit default prediction, as an important task in financial risk management, has received increasing attention. Traditional credit default prediction methods mostly rely on machine learning models, such as decision trees and random forests, but these methods have certain...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	520,326
2007.10873	Connecting Embeddings for Knowledge Graph Entity Typing	Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	188,403
2411.02135	AI-Ready Energy Modelling for Next Generation RAN	Recent sustainability drives place energy-consumption metrics in centre-stage for the design of future radio access networks (RAN). At the same time, optimising the trade-off between performance and system energy usage by machine-learning (ML) is an approach that requires large amounts of granular RAN data to train mod...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	505,369
2311.06703	Enabling Human-Centered AI: A Methodological Perspective	Human-centered AI (HCAI) is a design philosophy that advocates prioritizing humans in designing, developing, and deploying intelligent systems, aiming to maximize the benefits of AI to humans and avoid potential adverse impacts. While HCAI continues to influence, the lack of guidance on methodology in practice makes it...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	true	407,041
2302.08493	Deep Multi-stream Network for Video-based Calving Sign Detection	We have designed a deep multi-stream network for automatically detecting calving signs from video. Calving sign detection from a camera, which is a non-contact sensor, is expected to enable more efficient livestock management. As large-scale, well-developed data cannot generally be assumed when establishing calving det...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	346,066
2112.15545	Training and Generating Neural Networks in Compressed Weight Space	The inputs and/or outputs of some neural nets are weight matrices of other neural nets. Indirect encodings or end-to-end compression of weight matrices could help to scale such approaches. Our goal is to open a discussion on this topic, starting with recurrent neural networks for character-level language modelling whos...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	273,818
1810.12794	Divergence Network: Graphical calculation method of divergence functions	In this paper, we introduce directed networks called `divergence network' in order to perform graphical calculation of divergence functions. By using the divergence networks, we can easily understand the geometric meaning of calculation results and grasp relations among divergence functions intuitively.	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	111,849
2404.02648	A Universal Deep Neural Network for Signal Detection in Wireless Communication Systems	Recently, deep learning (DL) has been emerging as a promising approach for channel estimation and signal detection in wireless communications. The majority of the existing studies investigating the use of DL techniques in this domain focus on analysing channel impulse responses that are generated from only one channel ...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	true	443,950
2111.11547	Camera Measurement of Physiological Vital Signs	The need for remote tools for healthcare monitoring has never been more apparent. Camera measurement of vital signs leverages imaging devices to compute physiological changes by analyzing images of the human body. Building on advances in optics, machine learning, computer vision and medicine these techniques have progr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	267,698
2103.01203	Generating Probabilistic Safety Guarantees for Neural Network Controllers	Neural networks serve as effective controllers in a variety of complex settings due to their ability to represent expressive policies. The complex nature of neural networks, however, makes their output difficult to verify and predict, which limits their use in safety-critical applications. While simulations provide ins...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	222,541
2109.07409	Sporting the government: Twitter as a window into sportspersons' engagement with causes in India and USA	With the ubiquitous reach of social media, influencers are increasingly central to articulation of political agendas on a range of topics. We curate a sample of tweets from the 200 most followed sportspersons in India and the United States respectively since 2019, map their connections with politicians, and visualize t...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	255,512
2209.11943	Planning for Multi-Object Manipulation with Graph Neural Network Relational Classifiers	Objects rarely sit in isolation in human environments. As such, we'd like our robots to reason about how multiple objects relate to one another and how those relations may change as the robot interacts with the world. To this end, we propose a novel graph neural network framework for multi-object manipulation to predic...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	319,356
2011.14126	Risk-Monotonicity in Statistical Learning	Acquisition of data is a difficult task in many applications of machine learning, and it is only natural that one hopes and expects the population risk to decrease (better performance) monotonically with increasing data points. It turns out, somewhat surprisingly, that this is not the case even for the most standard al...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,676
2212.14574	X-MAS: Extremely Large-Scale Multi-Modal Sensor Dataset for Outdoor Surveillance in Real Environments	In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Exi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	338,660
1710.06495	A Line-Point Unified Solution to Relative Camera Pose Estimation	In this work we present a unified method of relative camera pose estimation from points and lines correspondences. Given a set of 2D points and lines correspondences in three views, of which two are known, a method has been developed for estimating the camera pose of the third view. Novelty of this algorithm is to comb...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	82,779
2407.16328	Improving multidimensional projection quality with user-specific metrics and optimal scaling	The growing prevalence of high-dimensional data has fostered the development of multidimensional projection (MP) techniques, such as t-SNE, UMAP, and LAMP, for data visualization and exploration. However, conventional MP methods typically employ generic quality metrics, neglecting individual user preferences. This stud...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	475,561
2501.04329	An Efficient Adaptive Compression Method for Human Perception and Machine Vision Tasks	While most existing neural image compression (NIC) and neural video compression (NVC) methodologies have achieved remarkable success, their optimization is primarily focused on human visual perception. However, with the rapid development of artificial intelligence, many images and videos will be used for various machin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	523,186
2004.02854	Projected Push-Sum Gradient Descent-Ascent for Convex Optimizationwith Application to Economic Dispatch Problems	We propose a novel algorithm for solving convex, constrained and distributed optimization problems defined on multi-agent-networks, where each agent has exclusive access to a part of the global objective function. The agents are able to exchange information over a directed, weighted communication graph, which can be re...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	171,364
2107.09313	SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models	For successful scene text recognition (STR) models, synthetic text image generators have alleviated the lack of annotated text images from the real world. Specifically, they generate multiple text images with diverse backgrounds, font styles, and text shapes and enable STR models to learn visual patterns that might not...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	246,999
2104.10325	SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation	Deep CNNs have achieved significant successes in image processing and its applications, including single image super-resolution (SR). However, conventional methods still resort to some predetermined integer scaling factors, e.g., x2 or x4. Thus, they are difficult to be applied when arbitrary target resolutions are req...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,534
2211.04888	Extending Temporal Data Augmentation for Video Action Recognition	Pixel space augmentation has grown in popularity in many Deep Learning areas, due to its effectiveness, simplicity, and low computational cost. Data augmentation for videos, however, still remains an under-explored research topic, as most works have been treating inputs as stacks of static images rather than temporally...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	329,377
2306.12594	State-wise Constrained Policy Optimization	Reinforcement Learning (RL) algorithms have shown tremendous success in simulation environments, but their application to real-world problems faces significant challenges, with safety being a major concern. In particular, enforcing state-wise constraints is essential for many challenging tasks such as autonomous drivin...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	374,988
2201.10945	On the Power of Gradual Network Alignment Using Dual-Perception Similarities	Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched throug...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	true	false	true	277,144
2010.00717	Deep Reinforcement Learning with Mixed Convolutional Network	Recent research has shown that map raw pixels from a single front-facing camera directly to steering commands are surprisingly powerful. This paper presents a convolutional neural network (CNN) to playing the CarRacing-v0 using imitation learning in OpenAI Gym. The dataset is generated by playing the game manually in G...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	198,366
2406.11092	Guaranteed Sampling Flexibility for Low-tubal-rank Tensor Completion	While Bernoulli sampling is extensively studied in tensor completion, t-CUR sampling approximates low-tubal-rank tensors via lateral and horizontal subtensors. However, both methods lack sufficient flexibility for diverse practical applications. To address this, we introduce Tensor Cross-Concentrated Sampling (t-CCS), ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	464,711
2009.14181	Policies for Multi-Agency Recovery of Physical Infrastructure After Disasters	We consider a scenario where multiple infrastructure components have been damaged after a disaster and the health value of each component continues to deteriorate if it is not being targeted by a repair agency, until it fails irreversibly. There are multiple agencies that seek to repair the components and there is an a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	197,961
2410.10779	Focused ReAct: Improving ReAct through Reiterate and Early Stop	Large language models (LLMs) have significantly improved their reasoning and decision-making capabilities, as seen in methods like ReAct. However, despite its effectiveness in tackling complex tasks, ReAct faces two main challenges: losing focus on the original question and becoming stuck in action loops. To address th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	498,224
1805.02991	Differential Equations for Modeling Asynchronous Algorithms	Asynchronous stochastic gradient descent (ASGD) is a popular parallel optimization algorithm in machine learning. Most theoretical analysis on ASGD take a discrete view and prove upper bounds for their convergence rates. However, the discrete view has its intrinsic limitations: there is no characterization of the optim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	96,963
2405.05417	Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models	The disconnect between tokenizer creation and model training in language models allows for specific inputs, such as the infamous SolidGoldMagikarp token, to induce unwanted model behaviour. Although such `glitch tokens', tokens present in the tokenizer vocabulary but that are nearly or entirely absent during model trai...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	452,903
2304.04027	NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic Radiographs	Panoramic radiography (Panoramic X-ray, PX) is a widely used imaging modality for dental examination. However, PX only provides a flattened 2D image, lacking in a 3D view of the oral structure. In this paper, we propose NeBLa (Neural Beer-Lambert) to estimate 3D oral structures from real-world PX. NeBLa tackles full 3D...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	357,043
1811.08338	Causal Inference by String Diagram Surgery	Extracting causal relationships from observed correlations is a growing area in probabilistic reasoning, originating with the seminal work of Pearl and others from the early 1990s. This paper develops a new, categorically oriented view based on a clear distinction between syntax (string diagrams) and semantics (stochas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	114,016
2011.11732	Detecting hidden signs of diabetes in external eye photographs	Diabetes-related retinal conditions can be detected by examining the posterior of the eye. By contrast, examining the anterior of the eye can reveal conditions affecting the front of the eye, such as changes to the eyelids, cornea, or crystalline lens. In this work, we studied whether external photographs of the front ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	207,917
2009.01616	Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images	In recent years, there are many applications of object detection in remote sensing field, which demands a great number of labeled data. However, in many cases, data is extremely rare. In this paper, we proposed a few-shot object detector which is designed for detecting novel objects based on only a few examples. Throug...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	194,355
2204.10407	Improving Distribution System Resilience by Undergrounding Lines and Deploying Mobile Generators	To improve the resilience of electric distribution systems, this paper proposes a stochastic multi-period mixed-integer linear programming model that determines where to underground distribution lines and how to coordinate mobile generators in order to serve critical loads during extreme events. The proposed model repr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	292,768
2003.13450	A Novel Fuzzy Approximate Reasoning Method Based on Extended Distance Measure in SISO Fuzzy System	This paper presents an original method of fuzzy approximate reasoning that can open a new direction of research in the uncertainty inference of Artificial Intelligence(AI) and Computational Intelligence(CI). Fuzzy modus ponens (FMP) and fuzzy modus tollens(FMT) are two fundamental and basic models of general fuzzy appr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	170,213
2406.18564	Rotation Averaging: A Primal-Dual Method and Closed-Forms in Cycle Graphs	A cornerstone of geometric reconstruction, rotation averaging seeks the set of absolute rotations that optimally explains a set of measured relative orientations between them. In addition to being an integral part of bundle adjustment and structure-from-motion, the problem of synchronizing rotations also finds applicat...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	468,073
2211.07889	Pretraining ECG Data with Adversarial Masking Improves Model Generalizability for Data-Scarce Tasks	Medical datasets often face the problem of data scarcity, as ground truth labels must be generated by medical professionals. One mitigation strategy is to pretrain deep learning models on large, unlabelled datasets with self-supervised learning (SSL). Data augmentations are essential for improving the generalizability ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	330,406
1810.05474	Pre-gen metrics: Predicting caption quality metrics without generating captions	Image caption generation systems are typically evaluated against reference outputs. We show that it is possible to predict output quality without generating the captions, based on the probability assigned by the neural model to the reference captions. Such pre-gen metrics are strongly correlated to standard evaluation ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	110,237
2404.16678	Multimodal Semantic-Aware Automatic Colorization with Diffusion Prior	Colorizing grayscale images offers an engaging visual experience. Existing automatic colorization methods often fail to generate satisfactory results due to incorrect semantic colors and unsaturated colors. In this work, we propose an automatic colorization pipeline to overcome these challenges. We leverage the extraor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,584
2409.11580	PLATO: Planning with LLMs and Affordances for Tool Manipulation	As robotic systems become increasingly integrated into complex real-world environments, there is a growing need for approaches that enable robots to understand and act upon natural language instructions without relying on extensive pre-programmed knowledge of their surroundings. This paper presents PLATO, an innovative...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	489,219
2112.01476	KPDrop: Improving Absent Keyphrase Generation	Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. Keyphrases can be either present or absent from the given document. While the extraction of present keyphrases has received much attention in the past, only recently a stronger focus has been placed o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	269,494
2306.04971	A Melting Pot of Evolution and Learning	We survey eight recent works by our group, involving the successful blending of evolutionary algorithms with machine learning and deep learning: 1. Binary and Multinomial Classification through Evolutionary Symbolic Regression, 2. Classy Ensemble: A Novel Ensemble Algorithm for Classification, 3. EC-KitY: Evolutionary ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	371,996
2111.04894	Safe Policy Optimization with Local Generalized Linear Function Approximations	Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems. Existing safe exploration methods guaranteed safety under the assumption of regularity, and it has been difficult to apply them to large-scale real problems. We propose a novel algorithm, SPO-LF, that optimizes an agent's poli...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	265,631
2109.05201	Conditional Generation of Synthetic Geospatial Images from Pixel-level and Feature-level Inputs	Training robust supervised deep learning models for many geospatial applications of computer vision is difficult due to dearth of class-balanced and diverse training data. Conversely, obtaining enough training data for many applications is financially prohibitive or may be infeasible, especially when the application in...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	254,702
2011.07495	FAIR: Fair Adversarial Instance Re-weighting	With growing awareness of societal impact of artificial intelligence, fairness has become an important aspect of machine learning algorithms. The issue is that human biases towards certain groups of population, defined by sensitive features like race and gender, are introduced to the training data through data collecti...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	206,580
1904.01614	Persistent Memory I/O Primitives	I/O latency and throughput is one of the major performance bottlenecks for disk-based database systems. Upcoming persistent memory (PMem) technologies, like Intel's Optane DC Persistent Memory Modules, promise to bridge the gap between NAND-based flash (SSD) and DRAM, and thus eliminate the I/O bottleneck. In this pape...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	126,185
2107.01165	Dissipativity-based $\mathcal{L}_2$ gain-scheduled static output feedback design for rational LPV systems	This paper proposes the design of gain-scheduled static output feedback controllers for the stabilization of continuous-time linear parameter-varying systems with $\mathcal{L}_2$-gain performance. The system is transformed into the form of a differential-algebraic representation which allows dealing with the broad clas...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	244,390
2406.08858	OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning	We present OmniH2O (Omni Human-to-Humanoid), a learning-based system for whole-body humanoid teleoperation and autonomy. Using kinematic pose as a universal control interface, OmniH2O enables various ways for a human to control a full-sized humanoid with dexterous hands, including using real-time teleoperation through ...	false	false	false	false	false	false	true	true	false	false	true	true	false	false	false	false	false	false	463,663
2310.17325	C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder	Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to discover them in the latent space. These factors are expected to be causally disentangled, meaning that distinct factors are encoded into separate latent variabl...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	403,086
1511.07551	Transductive Log Opinion Pool of Gaussian Process Experts	We introduce a framework for analyzing transductive combination of Gaussian process (GP) experts, where independently trained GP experts are combined in a way that depends on test point location, in order to scale GPs to big data. The framework provides some theoretical justification for the generalized product of GP e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	49,444
1601.01909	Delivery Time Reduction for Order-Constrained Applications using Binary Network Codes	Consider a radio access network wherein a base-station is required to deliver a set of order-constrained messages to a set of users over independent erasure channels. This paper studies the delivery time reduction problem using instantly decodable network coding (IDNC). Motivated by time-critical and order-constrained ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	50,781
1905.04964	Exogenous Rewards for Promoting Cooperation in Scale-Free Networks	The design of mechanisms that encourage pro-social behaviours in populations of self-regarding agents is recognised as a major theoretical challenge within several areas of social, life and engineering sciences. When interference from external parties is considered, several heuristics have been identified as capable of...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	true	130,607
1308.3432	Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation	Stochastic neurons and hard non-linearities can be useful for a number of reasons in deep learning models, but in many cases they pose a challenging problem: how to estimate the gradient of a loss function with respect to the input of such stochastic or non-smooth neurons? I.e., can we "back-propagate" through these st...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	26,468
2304.00690	3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds	Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) model is largely neglected as most existing benchmarks are dominated by point clouds captured under normal weather. We introduce SemanticSTF, an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	355,773
2308.16477	PivotNet: Vectorized Pivot Learning for End-to-end HD Map Construction	Vectorized high-definition map online construction has garnered considerable attention in the field of autonomous driving research. Most existing approaches model changeable map elements using a fixed number of points, or predict local maps in a two-stage autoregressive manner, which may miss essential details and lead...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	389,000
2412.17804	GauSim: Registering Elastic Objects into Digital World by Gaussian Simulator	In this work, we introduce GauSim, a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels. Unlike traditional methods that treat kernels as particles within particle-based simulations, we leverage continuum mechanics, modeling ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	520,118
2105.01706	Sampling From the Wasserstein Barycenter	This work presents an algorithm to sample from the Wasserstein barycenter of absolutely continuous measures. Our method is based on the gradient flow of the multimarginal formulation of the Wasserstein barycenter, with an additive penalization to account for the marginal constraints. We prove that the minimum of this p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	233,594
1710.03344	Iterative PET Image Reconstruction Using Convolutional Neural Network Representation	PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this work, we trained a deep residual convol...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	82,311
2106.08512	Revisit Visual Representation in Analytics Taxonomy: A Compression Perspective	Visual analytics have played an increasingly critical role in the Internet of Things, where massive visual signals have to be compressed and fed into machines. But facing such big data and constrained bandwidth capacity, existing image/video compression methods lead to very low-quality representations, while existing f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	241,319
1709.00725	Blind Stereo Image Quality Assessment Inspired by Brain Sensory-Motor Fusion	The use of 3D and stereo imaging is rapidly increasing. Compression, transmission, and processing could degrade the quality of stereo images. Quality assessment of such images is different than their 2D counterparts. Metrics that represent 3D perception by human visual system (HVS) are expected to assess stereoscopic q...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,959
2206.10957	Ordered-Statistics Decoding with Adaptive Gaussian Elimination Reduction for Short Codes	In this paper, we propose an efficient ordered-statistics decoding (OSD) algorithm with an adaptive Gaussian elimination (GE) reduction technique. The proposed decoder utilizes two decoding conditions to adaptively remove GE in OSD. The first condition determines whether GE could be skipped in the OSD process by estima...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	304,099
1010.5764	(2,1)-separating systems beyond the probabilistic bound	Building on previous results of Xing, we give new lower bounds on the rate of intersecting codes over large alphabets. The proof is constructive, and uses algebraic geometry, although nothing beyond the basic theory of linear systems on curves. Then, using these new bounds within a concatenation argument, we construct ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,051
2412.15023	Stable-V2A: Synthesis of Synchronized Sound Effects with Temporal and Semantic Controls	Sound designers and Foley artists usually sonorize a scene, such as from a movie or video game, by manually annotating and sonorizing each action of interest in the video. In our case, the intent is to leave full creative control to sound designers with a tool that allows them to bypass the more repetitive parts of the...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	518,924
2501.06597	EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses	The widespread adoption of generative AI has generated diverse opinions, with individuals expressing both support and criticism of its applications. This study investigates the emotional dynamics surrounding generative AI by analyzing human tweets referencing terms such as ChatGPT, OpenAI, Copilot, and LLMs. To further...	true	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	524,049
2101.06133	Teaming up with information agents	Despite the intricacies involved in designing a computer as a teampartner, we can observe patterns in team behavior which allow us to describe at a general level how AI systems are to collaborate with humans. Whereas most work on human-machine teaming has focused on physical agents (e.g. robotic systems), our aim is to...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	215,620
1805.02579	30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine	Heretofore, global burned area (BA) products are only available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on automated...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	96,886
1810.09485	Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions	We demonstrate how efficiency of Cartesian Genetic Programming method can be scaled up through the preferential selection of phenotypically larger solutions, i.e. through the preferential selection of larger solutions among equally good solutions. The advantage of the preferential selection of larger solutions is valid...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	111,063
2308.02805	Meta-Analysis and Systematic Review for Anomaly Network Intrusion Detection Systems: Detection Methods, Dataset, Validation Methodology, and Challenges	Intrusion detection systems (IDSs) built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. Although review papers are used the systematic review or simple methods to analyse and criticize the anomaly NIDS works, the current review uses a trad...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	383,777
2108.02360	Exploring Structure Consistency for Deep Model Watermarking	The intellectual property (IP) of Deep neural networks (DNNs) can be easily ``stolen'' by surrogate model attack. There has been significant progress in solutions to protect the IP of DNN models in classification tasks. However, little attention has been devoted to the protection of DNNs in image processing tasks. By u...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	249,295
2411.11213	Making Sigmoid-MSE Great Again: Output Reset Challenges Softmax Cross-Entropy in Neural Network Classification	This study presents a comparative analysis of two objective functions, Mean Squared Error (MSE) and Softmax Cross-Entropy (SCE) for neural network classification tasks. While SCE combined with softmax activation is the conventional choice for transforming network outputs into class probabilities, we explore an alternat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	508,958
2111.10773	One-shot Weakly-Supervised Segmentation in Medical Images	Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot segmentation and weakly-supervised learning are promising research directions that lower labeling effort by learning a new class from only one annotated image and ut...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,442
2010.05272	IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration	Point cloud is an important 3D data representation widely used in many essential applications. Leveraging deep neural networks, recent works have shown great success in processing 3D point clouds. However, those deep neural networks are vulnerable to various 3D adversarial attacks, which can be summarized as two primar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	200,055
2101.06644	HySTER: A Hybrid Spatio-Temporal Event Reasoner	The task of Video Question Answering (VideoQA) consists in answering natural language questions about a video and serves as a proxy to evaluate the performance of a model in scene sequence understanding. Most methods designed for VideoQA up-to-date are end-to-end deep learning architectures which struggle at complex te...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	true	215,795
1502.02590	Analysis of classifiers' robustness to adversarial perturbations	The goal of this paper is to analyze an intriguing phenomenon recently discovered in deep networks, namely their instability to adversarial perturbations (Szegedy et. al., 2014). We provide a theoretical framework for analyzing the robustness of classifiers to adversarial perturbations, and show fundamental upper bound...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	40,061
2408.08632	A Survey on Benchmarks of Multimodal Large Language Models	Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual question answering, visual perception, understanding, and reasoning. Over the past few years, significant efforts have been made to examine ...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	481,077

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