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Title: Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networks Abstract: Planning under uncertainty is an area of interest in artificial intelligence. We present a novel approach based on tree search and graph machine learning for th... |
Title: Learning Parameterized Task Structure for Generalization to Unseen Entities Abstract: Real world tasks are hierarchical and compositional. Tasks can be composed of multiple subtasks (or sub-goals) that are dependent on each other. These subtasks are defined in terms of entities (e.g., "apple", "pear") that can b... |
Title: Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation Abstract: Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a 'socially compliant' manner in the presence of other intelligent agents such as humans. With the eme... |
Title: AUC Maximization in the Era of Big Data and AI: A Survey Abstract: Area under the ROC curve, a.k.a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC score. It... |
Title: A Deep Learning Technique using a Sequence of Follow Up X-Rays for Disease classification Abstract: The ability to predict lung and heart based diseases using deep learning techniques is central to many researchers, particularly in the medical field around the world. In this paper, we present a unique outlook of... |
Title: Cycle-Consistent Counterfactuals by Latent Transformations Abstract: CounterFactual (CF) visual explanations try to find images similar to the query image that change the decision of a vision system to a specified outcome. Existing methods either require inference-time optimization or joint training with a gener... |
Title: User Driven Model Adjustment via Boolean Rule Explanations Abstract: AI solutions are heavily dependant on the quality and accuracy of the input training data, however the training data may not always fully reflect the most up-to-date policy landscape or may be missing business logic. The advances in explainabil... |
Title: Learning Optical Flow, Depth, and Scene Flow without Real-World Labels Abstract: Self-supervised monocular depth estimation enables robots to learn 3D perception from raw video streams. This scalable approach leverages projective geometry and ego-motion to learn via view synthesis, assuming the world is mostly s... |
Title: New pyramidal hybrid textural and deep features based automatic skin cancer classification model: Ensemble DarkNet and textural feature extractor Abstract: Background: Skin cancer is one of the widely seen cancer worldwide and automatic classification of skin cancer can be benefited dermatology clinics for an ac... |
Title: Improved singing voice separation with chromagram-based pitch-aware remixing Abstract: Singing voice separation aims to separate music into vocals and accompaniment components. One of the major constraints for the task is the limited amount of training data with separated vocals. Data augmentation techniques suc... |
Title: Robust Speaker Recognition with Transformers Using wav2vec 2.0 Abstract: Recent advances in unsupervised speech representation learning discover new approaches and provide new state-of-the-art for diverse types of speech processing tasks. This paper presents an investigation of using wav2vec 2.0 deep speech repr... |
Title: Understanding out-of-distribution accuracies through quantifying difficulty of test samples Abstract: Existing works show that although modern neural networks achieve remarkable generalization performance on the in-distribution (ID) dataset, the accuracy drops significantly on the out-of-distribution (OOD) datas... |
Title: FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation Abstract: Federated learning is a framework for distributed optimization that places emphasis on communication efficiency. In particular, it follows a client-server broadcast model and is particularly appealing because of its ability to ac... |
Title: Investigation of Different Calibration Methods for Deep Speaker Embedding based Verification Systems Abstract: Deep speaker embedding extractors have already become new state-of-the-art systems in the speaker verification field. However, the problem of verification score calibration for such systems often remain... |
Title: LiDAR Snowfall Simulation for Robust 3D Object Detection Abstract: 3D object detection is a central task for applications such as autonomous driving, in which the system needs to localize and classify surrounding traffic agents, even in the presence of adverse weather. In this paper, we address the problem of Li... |
Title: Toward Deep Learning Based Access Control Abstract: A common trait of current access control approaches is the challenging need to engineer abstract and intuitive access control models. This entails designing access control information in the form of roles (RBAC), attributes (ABAC), or relationships (ReBAC) as t... |
Title: Text2Pos: Text-to-Point-Cloud Cross-Modal Localization Abstract: Natural language-based communication with mobile devices and home appliances is becoming increasingly popular and has the potential to become natural for communicating with mobile robots in the future. Towards this goal, we investigate cross-modal ... |
Title: CMGAN: Conformer-based Metric GAN for Speech Enhancement Abstract: Recently, convolution-augmented transformer (Conformer) has achieved promising performance in automatic speech recognition (ASR) and time-domain speech enhancement (SE), as it can capture both local and global dependencies in the speech signal. I... |
Title: A super-polynomial lower bound for learning nonparametric mixtures Abstract: We study the problem of learning nonparametric distributions in a finite mixture, and establish a super-polynomial lower bound on the sample complexity of learning the component distributions in such models. Namely, we are given i.i.d. ... |
Title: A machine learning-based severity prediction tool for diabetic sensorimotor polyneuropathy using Michigan neuropathy screening instrumentations Abstract: Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-term complication in diabetic patients associated with painful neuropathy, foot ulcerat... |
Title: Practical Aspects of Zero-Shot Learning Abstract: One of important areas of machine learning research is zero-shot learning. It is applied when properly labeled training data set is not available. A number of zero-shot algorithms have been proposed and experimented with. However, none of them seems to be the "ov... |
Title: An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex Abstract: Word embedding is a modern distributed word representations approach widely used in many natural language processing tasks. Converting the vocabulary in a legal document into a word embedding model facilitates subjecting lega... |
Title: Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth Abstract: Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduce... |
Title: SPAct: Self-supervised Privacy Preservation for Action Recognition Abstract: Visual private information leakage is an emerging key issue for the fast growing applications of video understanding like activity recognition. Existing approaches for mitigating privacy leakage in action recognition require privacy lab... |
Title: Generalizing Few-Shot NAS with Gradient Matching Abstract: Efficient performance estimation of architectures drawn from large search spaces is essential to Neural Architecture Search. One-Shot methods tackle this challenge by training one supernet to approximate the performance of every architecture in the searc... |
Title: OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks Abstract: This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors. Specifically, we construct a distin... |
Title: SHOP: A Deep Learning Based Pipeline for near Real-Time Detection of Small Handheld Objects Present in Blurry Video Abstract: While prior works have investigated and developed computational models capable of object detection, models still struggle to reliably interpret images with motion blur and small objects. ... |
Title: Zero-Query Transfer Attacks on Context-Aware Object Detectors Abstract: Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural multi-object scenes is to impose a context-consistency check,... |
Title: Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets Abstract: Pooling multiple neuroimaging datasets across institutions often enables improvements in statistical power when evaluating associations (e.g., between risk factors and disease outcomes) that may otherwi... |
Title: Best Arm Identification in Restless Markov Multi-Armed Bandits Abstract: We study the problem of identifying the best arm in a multi-armed bandit environment when each arm is a time-homogeneous and ergodic discrete-time Markov process on a common, finite state space. The state evolution on each arm is governed b... |
Title: NeuraGen-A Low-Resource Neural Network based approach for Gender Classification Abstract: Human voice is the source of several important information. This is in the form of features. These Features help in interpreting various features associated with the speaker and speech. The speaker dependent work researcher... |
Title: Efficient Convex Optimization Requires Superlinear Memory Abstract: We show that any memory-constrained, first-order algorithm which minimizes $d$-dimensional, $1$-Lipschitz convex functions over the unit ball to $1/\mathrm{poly}(d)$ accuracy using at most $d^{1.25 - \delta}$ bits of memory must make at least $\... |
Title: Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection Abstract: Recent escalation in the field of computer vision underpins a huddle of algorithms with the magnificent potential to unravel the information contained within images. These computer vision algorithms are being practised in m... |
Title: A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis Abstract: Bearing fault identification and analysis is an important research area in the field of machinery fault diagnosis. Aiming at the common faults of rolling bearings, we propose a data-driven diagnostic algorithm b... |
Title: Mel Frequency Spectral Domain Defenses against Adversarial Attacks on Speech Recognition Systems Abstract: A variety of recent works have looked into defenses for deep neural networks against adversarial attacks particularly within the image processing domain. Speech processing applications such as automatic spe... |
Title: A Wavelet, AR and SVM based hybrid method for short-term wind speed prediction Abstract: Wind speed modelling and prediction has been gaining importance because of its significant roles in various stages of wind energy management. In this paper, we propose a hybrid model, based on wavelet transform to improve th... |
Title: Agreement or Disagreement in Noise-tolerant Mutual Learning? Abstract: Deep learning has made many remarkable achievements in many fields but suffers from noisy labels in datasets. The state-of-the-art learning with noisy label method Co-teaching and Co-teaching+ confronts the noisy label by mutual-information b... |
Title: Evolving Multi-Label Fuzzy Classifier Abstract: Multi-label classification has attracted much attention in the machine learning community to address the problem of assigning single samples to more than one class at the same time. We propose an evolving multi-label fuzzy classifier (EFC-ML) which is able to self-... |
Title: Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation Abstract: Neural Machine Translation (NMT) has reached a level of maturity to be recognized as the premier method for the translation between different languages and aroused interest in different research areas, includ... |
Title: syslrn: Learning What to Monitor for Efficient Anomaly Detection Abstract: While monitoring system behavior to detect anomalies and failures is important, existing methods based on log-analysis can only be as good as the information contained in the logs, and other approaches that look at the OS-level software s... |
Title: CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters Abstract: Currently, many theoretical as well as practically relevant questions towards the transferability and robustness of Convolutional Neural Networks (CNNs) remain unsolved. While ongoing research efforts are engaging these problems... |
Title: Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis Abstract: Synthesizing a subject-specific pathology-free image from a pathological image is valuable for algorithm development and clinical practice. In recent years, several approaches based on the Generative Adversarial Network (GAN) have ... |
Title: Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries Abstract: Learning under a continuously changing data distribution with incorrect labels is a desirable real-world problem yet challenging. A large body of continual learning (CL) methods, however, assumes data streams with clean... |
Title: Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning Abstract: Multiclass classification is of great interest for various machine learning applications, for example, it is a common task in computer vision, where one needs to categorize an image into three o... |
Title: Spoofing-Aware Speaker Verification by Multi-Level Fusion Abstract: Recently, many novel techniques have been introduced to deal with spoofing attacks, and achieve promising countermeasure (CM) performances. However, these works only take the stand-alone CM models into account. Nowadays, a spoofing aware speaker... |
Title: Pareto Set Learning for Neural Multi-objective Combinatorial Optimization Abstract: Multiobjective combinatorial optimization (MOCO) problems can be found in many real-world applications. However, exactly solving these problems would be very challenging, particularly when they are NP-hard. Many handcrafted heuri... |
Title: ReIL: A Framework for Reinforced Intervention-based Imitation Learning Abstract: Compared to traditional imitation learning methods such as DAgger and DART, intervention-based imitation offers a more convenient and sample efficient data collection process to users. In this paper, we introduce Reinforced Interven... |
Title: Physics-informed deep-learning applications to experimental fluid mechanics Abstract: High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general spars... |
Title: TransductGAN: a Transductive Adversarial Model for Novelty Detection Abstract: Novelty detection, a widely studied problem in machine learning, is the problem of detecting a novel class of data that has not been previously observed. A common setting for novelty detection is inductive whereby only examples of the... |
Title: AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement Abstract: Designing suitable deep model architectures, for AI-driven on-device apps and features, at par with rapidly evolving mobile hardware and increasingly complex target scenarios is a difficult task. Though Neural Architectu... |
Title: Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography Abstract: We present a method that lowers the dose required for a ptychographic reconstruction by adaptively scanning the specimen, thereby providing the required spatial information redundancy in the regions of highest importance. The... |
Title: On Reinforcement Learning, Effect Handlers, and the State Monad Abstract: We study the algebraic effects and handlers as a way to support decision-making abstractions in functional programs, whereas a user can ask a learning algorithm to resolve choices without implementing the underlying selection mechanism, an... |
Title: Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus Abstract: Training a text-to-speech (TTS) model requires a large scale text labeled speech corpus, which is troublesome to collect. In this paper, we propose a transfer learning framework for TTS that utilizes... |
Title: Protein language models trained on multiple sequence alignments learn phylogenetic relationships Abstract: Self-supervised neural language models with attention have recently been applied to biological sequence data, advancing structure, function and mutational effect prediction. Some protein language models, in... |
Title: Machine Composition of Korean Music via Topological Data Analysis and Artificial Neural Network Abstract: Common AI music composition algorithms based on artificial neural networks are to train a machine by feeding a large number of music pieces and create artificial neural networks that can produce music simila... |
Title: Abstract Flow for Temporal Semantic Segmentation on the Permutohedral Lattice Abstract: Semantic segmentation is a core ability required by autonomous agents, as being able to distinguish which parts of the scene belong to which object class is crucial for navigation and interaction with the environment. Approac... |
Title: Graph similarity learning for change-point detection in dynamic networks Abstract: Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectome, population flows and messages exchanges. In this work, we consider dynamic networks that are temporal sequences of graph snaps... |
Title: Gaussian Control Barrier Functions : A Non-Parametric Paradigm to Safety Abstract: Inspired by the success of control barrier functions (CBFs) in addressing safety, and the rise of data-driven techniques for modeling functions, we propose a non-parametric approach for online synthesis of CBFs using Gaussian Proc... |
Title: Over-the-Air Federated Learning via Second-Order Optimization Abstract: Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and security guarantees. However, FL could result in task-orient... |
Title: Neural representation of a time optimal, constant acceleration rendezvous Abstract: We train neural models to represent both the optimal policy (i.e. the optimal thrust direction) and the value function (i.e. the time of flight) for a time optimal, constant acceleration low-thrust rendezvous. In both cases we de... |
Title: Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping Abstract: A major hurdle for students and professional software developers who want to enter the world of machine learning (ML), is mastering not just the scientific background but also the available ML APIs. Therefore, we address... |
Title: Powerful Physical Adversarial Examples Against Practical Face Recognition Systems Abstract: It is well-known that the most existing machine learning (ML)-based safety-critical applications are vulnerable to carefully crafted input instances called adversarial examples (AXs). An adversary can conveniently attack ... |
Title: Deep Learning for Encrypted Traffic Classification and Unknown Data Detection Abstract: Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a new Deep Neural Network (D... |
Title: Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning Abstract: Machine learning (ML) models that use deep neural networks are vulnerable to backdoor attacks. Such attacks involve the insertion of a (hidden) trigger by an adversary. As a consequence, any input that contains the tr... |
Title: Improving Contrastive Learning with Model Augmentation Abstract: The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity and noise issues in sequences, a new self-supervised learning (SSL) p... |
Title: Achieving Guidance in Applied Machine Learning through Software Engineering Techniques Abstract: Development of machine learning (ML) applications is hard. Producing successful applications requires, among others, being deeply familiar with a variety of complex and quickly evolving application programming interf... |
Title: Explaining random forest prediction through diverse rulesets Abstract: Tree-ensemble algorithms, such as random forest, are effective machine learning methods popular for their flexibility, high performance, and robustness to overfitting. However, since multiple learners are combined,they are not as interpretabl... |
Title: Human Response to an AI-Based Decision Support System: A User Study on the Effects of Accuracy and Bias Abstract: Artificial Intelligence (AI) is increasingly used to build Decision Support Systems (DSS) across many domains. This paper describes a series of experiments designed to observe human response to diffe... |
Title: Rich Feature Construction for the Optimization-Generalization Dilemma Abstract: There often is a dilemma between ease of optimization and robust out-of-distribution (OoD) generalization. For instance, many OoD methods rely on penalty terms whose optimization is challenging. They are either too strong to optimize... |
Title: A multimodal approach for Parkinson disease analysis Abstract: Parkinson's disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1-1 %, and an annual incidence between 1.3-2.0/10000 inhabitants. The mean age at diagnosis of PD is 55 and most patien... |
Title: Learning neural audio features without supervision Abstract: Deep audio classification, traditionally cast as training a deep neural network on top of mel-filterbanks in a supervised fashion, has recently benefited from two independent lines of work. The first one explores "learnable frontends", i.e., neural mod... |
Title: Treatment Learning Transformer for Noisy Image Classification Abstract: Current top-notch deep learning (DL) based vision models are primarily based on exploring and exploiting the inherent correlations between training data samples and their associated labels. However, a known practical challenge is their degra... |
Title: BARC: Learning to Regress 3D Dog Shape from Images by Exploiting Breed Information Abstract: Our goal is to recover the 3D shape and pose of dogs from a single image. This is a challenging task because dogs exhibit a wide range of shapes and appearances, and are highly articulated. Recent work has proposed to di... |
Title: Graph Neural Networks are Dynamic Programmers Abstract: Recent advances in neural algorithmic reasoning with graph neural networks (GNNs) are propped up by the notion of algorithmic alignment. Broadly, a neural network will be better at learning to execute a reasoning task (in terms of sample complexity) if its ... |
Title: ME-CapsNet: A Multi-Enhanced Capsule Networks with Routing Mechanism Abstract: Convolutional Neural Networks need the construction of informative features, which are determined by channel-wise and spatial-wise information at the network's layers. In this research, we focus on bringing in a novel solution that us... |
Title: Invariance Learning based on Label Hierarchy Abstract: Deep Neural Networks inherit spurious correlations embedded in training data and hence may fail to predict desired labels on unseen domains (or environments), which have different distributions from the domain used in training. Invariance Learning (IL) has b... |
Title: Training Compute-Optimal Large Language Models Abstract: We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling langu... |
Title: Wildfire risk forecast: An optimizable fire danger index Abstract: Wildfire events have caused severe losses in many places around the world and are expected to increase with climate change. Throughout the years many technologies have been developed to identify fire events early on and to simulate fire behavior ... |
Title: Attacker Attribution of Audio Deepfakes Abstract: Deepfakes are synthetically generated media often devised with malicious intent. They have become increasingly more convincing with large training datasets advanced neural networks. These fakes are readily being misused for slander, misinformation and fraud. For ... |
Title: A Dataset for Speech Emotion Recognition in Greek Theatrical Plays Abstract: Machine learning methodologies can be adopted in cultural applications and propose new ways to distribute or even present the cultural content to the public. For instance, speech analytics can be adopted to automatically generate subtit... |
Title: Circuit encapsulation for efficient quantum computing based on controlled many-body dynamics Abstract: Controlling the time evolution of interacting spin systems is an important approach of implementing quantum computing. Different from the approaches by compiling the circuits into the product of multiple elemen... |
Title: Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition Abstract: Phonotactic constraints can be employed to distinguish languages by representing a speech utterance as a multinomial distribution or phone events. In the present study, we propose a new learning mechanism based on su... |
Title: Disentangling speech from surroundings in a neural audio codec Abstract: We present a method to separate speech signals from noisy environments in the compressed domain of a neural audio codec. We introduce a new training procedure that allows our model to produce structured encodings of audio waveforms given by... |
Title: Discovering Governing Equations by Machine Learning implemented with Invariance Abstract: The partial differential equation (PDE) plays a significantly important role in many fields of science and engineering. The conventional case of the derivation of PDE mainly relies on first principles and empirical observat... |
Title: Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review Abstract: The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during rou... |
Title: On Kernelized Multi-Armed Bandits with Constraints Abstract: We study a stochastic bandit problem with a general unknown reward function and a general unknown constraint function. Both functions can be non-linear (even non-convex) and are assumed to lie in a reproducing kernel Hilbert space (RKHS) with a bounded... |
Title: Photographic Visualization of Weather Forecasts with Generative Adversarial Networks Abstract: Outdoor webcam images are an information-dense yet accessible visualization of past and present weather conditions, and are consulted by meteorologists and the general public alike. Weather forecasts, however, are stil... |
Title: LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT Abstract: Self-supervised speech representation learning has shown promising results in various speech processing tasks. However, the pre-trained models, e.g., HuBERT, are storage-intensive Transformers, l... |
Title: Stochastic Conservative Contextual Linear Bandits Abstract: Many physical systems have underlying safety considerations that require that the strategy deployed ensures the satisfaction of a set of constraints. Further, often we have only partial information on the state of the system. We study the problem of saf... |
Title: Convex Non-negative Matrix Factorization Through Quantum Annealing Abstract: In this paper we provide the quantum version of the Convex Non-negative Matrix Factorization algorithm (Convex-NMF) by using the D-wave quantum annealer. More precisely, we use D-wave 2000Q to find the low rank approximation of a fixed ... |
Title: BASiNETEntropy: an alignment-free method for classification of biological sequences through complex networks and entropy maximization Abstract: The discovery of nucleic acids and the structure of DNA have brought considerable advances in the understanding of life. The development of next-generation sequencing te... |
Title: Diffusion Models for Counterfactual Explanations Abstract: Counterfactual explanations have shown promising results as a post-hoc framework to make image classifiers more explainable. In this paper, we propose DiME, a method allowing the generation of counterfactual images using the recent diffusion models. By l... |
Title: Nix-TTS: An Incredibly Lightweight End-to-End Text-to-Speech Model via Non End-to-End Distillation Abstract: We propose Nix-TTS, a lightweight neural TTS (Text-to-Speech) model achieved by applying knowledge distillation to a powerful yet large-sized generative TTS teacher model. Distilling a TTS model might sou... |
Title: Gaze-based Object Detection in the Wild Abstract: In human-robot collaboration, one challenging task is to teach a robot new yet unknown objects. Thereby, gaze can contain valuable information. We investigate if it is possible to detect objects (object or no object) from gaze data and determine their bounding bo... |
Title: Consistency regularization-based Deep Polynomial Chaos Neural Network Method for Reliability Analysis Abstract: Polynomial chaos expansion (PCE) is a powerful surrogate model-based reliability analysis method. Generally, a PCE model with a higher expansion order is usually required to obtain an accurate surrogat... |
Title: Nearly Minimax Algorithms for Linear Bandits with Shared Representation Abstract: We give novel algorithms for multi-task and lifelong linear bandits with shared representation. Specifically, we consider the setting where we play $M$ linear bandits with dimension $d$, each for $T$ rounds, and these $M$ bandit ta... |
Title: SurvCaus : Representation Balancing for Survival Causal Inference Abstract: Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years. Most of the time, individual effects are better presented as Conditional Average Treatment Effects (CATE). Recently, representation balanci... |
Title: Improved Counting and Localization from Density Maps for Object Detection in 2D and 3D Microscopy Imaging Abstract: Object counting and localization are key steps for quantitative analysis in large-scale microscopy applications. This procedure becomes challenging when target objects are overlapping, are densely ... |
Title: Contrasting the landscape of contrastive and non-contrastive learning Abstract: A lot of recent advances in unsupervised feature learning are based on designing features which are invariant under semantic data augmentations. A common way to do this is contrastive learning, which uses positive and negative sample... |
Title: Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems Abstract: In data-driven modeling of spatiotemporal phenomena careful consideration often needs to be made in capturing the dynamics of the high wavenumbers. This problem becomes especially challenging when the syste... |
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