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Title: How to Guide Adaptive Depth Sampling? Abstract: Recent advances in depth sensing technologies allow fast electronic maneuvering of the laser beam, as opposed to fixed mechanical rotations. This will enable future sensors, in principle, to vary in real-time the sampling pattern. We examine here the abstract probl... |
Title: The Fairness of Credit Scoring Models Abstract: In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they also often discriminate between individuals sharing a protected attribute (e.g. gender, age, racial origin) and the rest of the popula... |
Title: A Proximal Algorithm for Sampling from Non-convex Potentials Abstract: We study sampling problems associated with non-convex potentials that meanwhile lack smoothness. In particular, we consider target distributions that satisfy either logarithmic-Sobolev inequality or Poincar\'e inequality. Rather than smooth, ... |
Title: Bayesian Active Learning with Fully Bayesian Gaussian Processes Abstract: The bias-variance trade-off is a well-known problem in machine learning that only gets more pronounced the less available data there is. In active learning, where labeled data is scarce or difficult to obtain, neglecting this trade-off can... |
Title: Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review Abstract: With the recent success of computer vision and deep learning, remarkable progress has been achieved on automatic personal recognition using vein biometrics. However, collecting large-scale real-world training data for palm vein ... |
Title: Task Relabelling for Multi-task Transfer using Successor Features Abstract: Deep Reinforcement Learning has been very successful recently with various works on complex domains. Most works are concerned with learning a single policy that solves the target task, but is fixed in the sense that if the environment ch... |
Title: Diversity vs. Recognizability: Human-like generalization in one-shot generative models Abstract: Robust generalization to new concepts has long remained a distinctive feature of human intelligence. However, recent progress in deep generative models has now led to neural architectures capable of synthesizing nove... |
Title: AutoFedNLP: An efficient FedNLP framework Abstract: Transformer-based pre-trained models have revolutionized NLP for superior performance and generality. Fine-tuning pre-trained models for downstream tasks often require private data, for which federated learning is the de-facto approach (i.e., FedNLP). However, ... |
Title: Swapping Semantic Contents for Mixing Images Abstract: Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of available labeled data has become the principal bottleneck in low label settings such as Semi-Supervised Learning. Mixing Data Au... |
Title: Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens Abstract: It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variables... |
Title: Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application Abstract: In past years, the minimax type single-level optimization formulation and its variations have been widely utilized to address Generative Adversarial Networks (GANs). Unfortunately, it has been proved that these alter... |
Title: The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networks Abstract: In laboratory object recognition tasks based on undistorted photographs, both adult humans and Deep Neural Networks (DNNs) perform close to ceiling. Unlike adults', whose ob... |
Title: Towards efficient feature sharing in MIMO architectures Abstract: Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, these architectures are wasteful i... |
Title: FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels Abstract: Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Hence, the labels in practice are usually annotated by clients of varyi... |
Title: LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation Abstract: Combinatorial Optimisation problems arise in several application domains and are often formulated in terms of graphs. Many of these problems are NP-hard, but exact solutions are not always needed. Several heurist... |
Title: Visual Concepts Tokenization Abstract: Obtaining the human-like perception ability of abstracting visual concepts from concrete pixels has always been a fundamental and important target in machine learning research fields such as disentangled representation learning and scene decomposition. Towards this goal, we... |
Title: Kernel Normalized Convolutional Networks Abstract: Existing deep convolutional neural network (CNN) architectures frequently rely upon batch normalization (BatchNorm) to effectively train the model. BatchNorm significantly improves model performance, but performs poorly with smaller batch sizes. To address this ... |
Title: Semi-self-supervised Automated ICD Coding Abstract: Clinical Text Notes (CTNs) contain physicians' reasoning process, written in an unstructured free text format, as they examine and interview patients. In recent years, several studies have been published that provide evidence for the utility of machine learning... |
Title: A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models Abstract: We study experiment design for the unique identification of the causal graph of a system where the graph may contain cycles. The presence of cycles in the structure introduces major challenges for experiment design. Unlike the ca... |
Title: On Calibration of Ensemble-Based Credal Predictors Abstract: In recent years, several classification methods that intend to quantify epistemic uncertainty have been proposed, either by producing predictions in the form of second-order distributions or sets of probability distributions. In this work, we focus on ... |
Title: Deployment of Energy-Efficient Deep Learning Models on Cortex-M based Microcontrollers using Deep Compression Abstract: Large Deep Neural Networks (DNNs) are the backbone of today's artificial intelligence due to their ability to make accurate predictions when being trained on huge datasets. With advancing techn... |
Title: Unintended memorisation of unique features in neural networks Abstract: Neural networks pose a privacy risk due to their propensity to memorise and leak training data. We show that unique features occurring only once in training data are memorised by discriminative multi-layer perceptrons and convolutional neura... |
Title: On the Prediction Instability of Graph Neural Networks Abstract: Instability of trained models, i.e., the dependence of individual node predictions on random factors, can affect reproducibility, reliability, and trust in machine learning systems. In this paper, we systematically assess the prediction instability... |
Title: Understanding and Mitigating the Uncertainty in Zero-Shot Translation Abstract: Zero-shot translation is a promising direction for building a comprehensive multilingual neural machine translation (MNMT) system. However, its quality is still not satisfactory due to off-target issues. In this paper, we aim to unde... |
Title: The Unreasonable Effectiveness of Deep Evidential Regression Abstract: There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based on ... |
Title: Leveraging Relational Information for Learning Weakly Disentangled Representations Abstract: Disentanglement is a difficult property to enforce in neural representations. This might be due, in part, to a formalization of the disentanglement problem that focuses too heavily on separating relevant factors of varia... |
Title: A Case of Exponential Convergence Rates for SVM Abstract: Classification is often the first problem described in introductory machine learning classes. Generalization guarantees of classification have historically been offered by Vapnik-Chervonenkis theory. Yet those guarantees are based on intractable algorithm... |
Title: Towards Extremely Fast Bilevel Optimization with Self-governed Convergence Guarantees Abstract: Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in learning and vision fields. The validity of existing works heavily relies on solving a series of approximation subproblems with ext... |
Title: MaskGAE: Masked Graph Modeling Meets Graph Autoencoders Abstract: We present masked graph autoencoder (MaskGAE), a self-supervised learning framework for graph-structured data. Different from previous graph autoencoders (GAEs), MaskGAE adopts masked graph modeling (MGM) as a principled pretext task: masking a po... |
Title: The Sufficiency of Off-policyness: PPO is insufficient according to an Off-policy Measure Abstract: One of the major difficulties of reinforcement learning is learning from {\em off-policy} samples, which are collected by a different policy (behavior policy) from what the algorithm evaluates (the target policy).... |
Title: ExMo: Explainable AI Model using Inverse Frequency Decision Rules Abstract: In this paper, we present a novel method to compute decision rules to build a more accurate interpretable machine learning model, denoted as ExMo. The ExMo interpretable machine learning model consists of a list of IF...THEN... statement... |
Title: Towards biologically plausible Dreaming and Planning Abstract: Humans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performances. Recent model-based approaches show promising results by reducing th... |
Title: Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks Abstract: Monte Carlo (MC) integration is the de facto method for approximating the predictive distribution of Bayesian neural networks (BNNs). But, even with many MC samples, Gaussian-based BNNs could still yield bad predictive performa... |
Title: Exploring Extreme Parameter Compression for Pre-trained Language Models Abstract: Recent work explored the potential of large-scale Transformer-based pre-trained models, especially Pre-trained Language Models (PLMs) in natural language processing. This raises many concerns from various perspectives, e.g., financ... |
Title: Survey on Fair Reinforcement Learning: Theory and Practice Abstract: Fairness-aware learning aims at satisfying various fairness constraints in addition to the usual performance criteria via data-driven machine learning techniques. Most of the research in fairness-aware learning employs the setting of fair-super... |
Title: Trend analysis and forecasting air pollution in Rwanda Abstract: Air pollution is a major public health problem worldwide although the lack of data is a global issue for most low and middle income countries. Ambient air pollution in the form of fine particulate matter (PM2.5) exceeds the World Health Organizatio... |
Title: Towards Consistency in Adversarial Classification Abstract: In this paper, we study the problem of consistency in the context of adversarial examples. Specifically, we tackle the following question: can surrogate losses still be used as a proxy for minimizing the $0/1$ loss in the presence of an adversary that a... |
Title: Predicting electrode array impedance after one month from cochlear implantation surgery Abstract: Sensorineural hearing loss can be treated using Cochlear implantation. After this surgery using the electrode array impedance measurements, we can check the stability of the impedance value and the dynamic range. De... |
Title: Neural Additive Models for Nowcasting Abstract: Deep neural networks (DNNs) are one of the most highlighted methods in machine learning. However, as DNNs are black-box models, they lack explanatory power for their predictions. Recently, neural additive models (NAMs) have been proposed to provide this power while... |
Title: Translating Hanja historical documents to understandable Korean and English Abstract: The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals were originally written in an archaic Korean writing system, `Hanja', an... |
Title: Self-Paced Multi-Agent Reinforcement Learning Abstract: Curriculum reinforcement learning (CRL) aims to speed up learning of a task by changing gradually the difficulty of the task from easy to hard through control of factors such as initial state or environment dynamics. While automating CRL is well studied in ... |
Title: A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection Abstract: Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite these progresses, how to ensure ... |
Title: Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning Abstract: Machine learning models with explainable predictions are increasingly sought after, especially for real-world, mission-critical applications that require bias detection and risk mit... |
Title: The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation? Abstract: We consider the problem of estimating a rank-1 signal corrupted by structured rotationally invariant noise, and address the following question: how well do inference algorithms perform when the noise ... |
Title: Self-Supervised Depth Estimation with Isometric-Self-Sample-Based Learning Abstract: Managing the dynamic regions in the photometric loss formulation has been a main issue for handling the self-supervised depth estimation problem. Most previous methods have alleviated this issue by removing the dynamic regions i... |
Title: RiskLoc: Localization of Multi-dimensional Root Causes by Weighted Risk Abstract: Failures and anomalies in large-scale software systems are unavoidable incidents. When an issue is detected, operators need to quickly and correctly identify its location to facilitate a swift repair. In this work, we consider the ... |
Title: MPI: Evaluating and Inducing Personality in Pre-trained Language Models Abstract: Originated as a philosophical quest, personality discerns how individuals differ from each other in terms of thinking, feeling, and behaving. Towards building social machines that work with humans on a daily basis, we are motivated... |
Title: Planning with Diffusion for Flexible Behavior Synthesis Abstract: Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While conceptually simple, this com... |
Title: Set-based Meta-Interpolation for Few-Task Meta-Learning Abstract: Meta-learning approaches enable machine learning systems to adapt to new tasks given few examples by leveraging knowledge from related tasks. However, a large number of meta-training tasks are still required for generalization to unseen tasks duri... |
Title: Nonlinear motion separation via untrained generator networks with disentangled latent space variables and applications to cardiac MRI Abstract: In this paper, a nonlinear approach to separate different motion types in video data is proposed. This is particularly relevant in dynamic medical imaging (e.g. PET, MRI... |
Title: SafeNet: Mitigating Data Poisoning Attacks on Private Machine Learning Abstract: Secure multiparty computation (MPC) has been proposed to allow multiple mutually distrustful data owners to jointly train machine learning (ML) models on their combined data. However, the datasets used for training ML models might b... |
Title: HeadText: Exploring Hands-free Text Entry using Head Gestures by Motion Sensing on a Smart Earpiece Abstract: We present HeadText, a hands-free technique on a smart earpiece for text entry by motion sensing. Users input text utilizing only 7 head gestures for key selection, word selection, word commitment and wo... |
Title: FairNorm: Fair and Fast Graph Neural Network Training Abstract: Graph neural networks (GNNs) have been demonstrated to achieve state-of-the-art for a number of graph-based learning tasks, which leads to a rise in their employment in various domains. However, it has been shown that GNNs may inherit and even ampli... |
Title: Neuro-Symbolic Regex Synthesis Framework via Neural Example Splitting Abstract: Due to the practical importance of regular expressions (regexes, for short), there has been a lot of research to automatically generate regexes from positive and negative string examples. We tackle the problem of learning regexes fas... |
Title: A New Feature Selection Method for LogNNet and its Application for Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values Abstract: Since February-2020, the world has embarked on an intense struggle with the COVID-19 disease, and health systems have come under a tragic pressure as the disease tur... |
Title: On Tackling Explanation Redundancy in Decision Trees Abstract: Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models. The interpretability of decision trees motivates explainability approaches by so-called intrinsic interpretability, and it is at the core of recent proposal... |
Title: Actively Tracking the Optimal Arm in Non-Stationary Environments with Mandatory Probing Abstract: We study a novel multi-armed bandit (MAB) setting which mandates the agent to probe all the arms periodically in a non-stationary environment. In particular, we develop \texttt{TS-GE} that balances the regret guaran... |
Title: A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks Abstract: Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data. We consider the problem of quantifying the ... |
Title: A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning Abstract: The aim of path planning is to reach the goal from starting point by searching for the route of an agent. In the path planning, the routes may vary depending on the number of variables such that it is importan... |
Title: Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search Abstract: Greedy best-first search (GBFS) and A* search (A*) are popular algorithms for path-finding on large graphs. Both use so-called heuristic functions, which estimate how close a vertex is to the goal. While heuristic fun... |
Title: Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions Abstract: Augmenting algorithms with learned predictions is a promising approach for going beyond worst-case bounds. Dinitz, Im, Lavastida, Moseley, and Vassilvitskii~(2021) have demonstrated that a warm start with learned dua... |
Title: A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting Abstract: With the growth of transport modes, high traffic forecasting precision is required in intelligent transportation systems. Most previous works utilize the transformer architecture based on graph neural networks and atten... |
Title: Learning to Reverse DNNs from AI Programs Automatically Abstract: With the privatization deployment of DNNs on edge devices, the security of on-device DNNs has raised significant concern. To quantify the model leakage risk of on-device DNNs automatically, we propose NNReverse, the first learning-based method whi... |
Title: Explainable Supervised Domain Adaptation Abstract: Domain adaptation techniques have contributed to the success of deep learning. Leveraging knowledge from an auxiliary source domain for learning in labeled data-scarce target domain is fundamental to domain adaptation. While these techniques result in increasing... |
Title: Conformal Prediction with Temporal Quantile Adjustments Abstract: We develop Temporal Quantile Adjustment (TQA), a general method to construct efficient and valid prediction intervals (PIs) for regression on cross-sectional time series data. Such data is common in many domains, including econometrics and healthc... |
Title: Towards Explanation for Unsupervised Graph-Level Representation Learning Abstract: Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's ... |
Title: FIND:Explainable Framework for Meta-learning Abstract: Meta-learning is used to efficiently enable the automatic selection of machine learning models by combining data and prior knowledge. Since the traditional meta-learning technique lacks explainability, as well as shortcomings in terms of transparency and fai... |
Title: BayesPCN: A Continually Learnable Predictive Coding Associative Memory Abstract: Associative memory plays an important role in human intelligence and its mechanisms have been linked to attention in machine learning. While the machine learning community's interest in associative memories has recently been rekindl... |
Title: Cross Reconstruction Transformer for Self-Supervised Time Series Representation Learning Abstract: Unsupervised/self-supervised representation learning in time series is critical since labeled samples are usually scarce in real-world scenarios. Existing approaches mainly leverage the contrastive learning framewo... |
Title: CertiFair: A Framework for Certified Global Fairness of Neural Networks Abstract: We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness. Individual Fairness suggests that similar individuals with respect to a certain task are to be treated similarly by the decision m... |
Title: On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approach Abstract: Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In... |
Title: Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder Abstract: This paper focuses on anomaly detection for multivariate time series data in large-scale fluid handling plants with dynamic components, such as power generation, water treatment, and chemical ... |
Title: KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation Abstract: Relative positional embeddings (RPE) have received considerable attention since RPEs effectively model the relative distance among tokens and enable length extrapolation. We propose KERPLE, a framework that generalizes relative p... |
Title: Robust Expected Information Gain for Optimal Bayesian Experimental Design Using Ambiguity Sets Abstract: The ranking of experiments by expected information gain (EIG) in Bayesian experimental design is sensitive to changes in the model's prior distribution, and the approximation of EIG yielded by sampling will h... |
Title: Can Foundation Models Wrangle Your Data? Abstract: Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning. As these models continue to grow in size, innovations continue to push the boundaries of what these m... |
Title: Sparse Infinite Random Feature Latent Variable Modeling Abstract: We propose a non-linear, Bayesian non-parametric latent variable model where the latent space is assumed to be sparse and infinite dimensional a priori using an Indian buffet process prior. A posteriori, the number of instantiated dimensions in th... |
Title: Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome Abstract: Data augmentation plays a key role in modern machine learning pipelines. While numerous augmentation strategies have been studied in the context of computer vision and natural language processing, less is known for ... |
Title: Manifold-aligned Neighbor Embedding Abstract: In this paper, we introduce a neighbor embedding framework for manifold alignment. We demonstrate the efficacy of the framework using a manifold-aligned version of the uniform manifold approximation and projection algorithm. We show that our algorithm can learn an al... |
Title: Minimal Explanations for Neural Network Predictions Abstract: Explaining neural network predictions is known to be a challenging problem. In this paper, we propose a novel approach which can be effectively exploited, either in isolation or in combination with other methods, to enhance the interpretability of neu... |
Title: Estimating the frame potential of large-scale quantum circuit sampling using tensor networks up to 50 qubits Abstract: We develop numerical protocols for estimating the frame potential, the 2-norm distance between a given ensemble and the exact Haar randomness, using the \texttt{QTensor} platform. Our tensor-net... |
Title: Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits Abstract: We prove an instance independent (poly) logarithmic regret for stochastic contextual bandits with linear payoff. Previously, in \cite{chu2011contextual}, a lower bound of $\mathcal{O}(\sqrt{... |
Title: Let the Model Decide its Curriculum for Multitask Learning Abstract: Curriculum learning strategies in prior multi-task learning approaches arrange datasets in a difficulty hierarchy either based on human perception or by exhaustively searching the optimal arrangement. However, human perception of difficulty may... |
Title: Interpolating Compressed Parameter Subspaces Abstract: Inspired by recent work on neural subspaces and mode connectivity, we revisit parameter subspace sampling for shifted and/or interpolatable input distributions (instead of a single, unshifted distribution). We enforce a compressed geometric structure upon a ... |
Title: Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection Abstract: Time series anomaly detection is of critical importance for the reliable and efficient operation of real-world systems. Many anomaly detection models have been developed throughout the years based on various assumptions rega... |
Title: A Rule Search Framework for the Early Identification of Chronic Emergency Homeless Shelter Clients Abstract: This paper uses rule search techniques for the early identification of emergency homeless shelter clients who are at risk of becoming long term or chronic shelter users. Using a data set from a major Nort... |
Title: Beyond Labels: Visual Representations for Bone Marrow Cell Morphology Recognition Abstract: Analyzing and inspecting bone marrow cell cytomorphology is a critical but highly complex and time-consuming component of hematopathology diagnosis. Recent advancements in artificial intelligence have paved the way for th... |
Title: Real Time Multi-Object Detection for Helmet Safety Abstract: The National Football League and Amazon Web Services teamed up to develop the best sports injury surveillance and mitigation program via the Kaggle competition. Through which the NFL wants to assign specific players to each helmet, which would help acc... |
Title: Incremental Learning with Differentiable Architecture and Forgetting Search Abstract: As progress is made on training machine learning models on incrementally expanding classification tasks (i.e., incremental learning), a next step is to translate this progress to industry expectations. One technique missing fro... |
Title: Content-Context Factorized Representations for Automated Speech Recognition Abstract: Deep neural networks have largely demonstrated their ability to perform automated speech recognition (ASR) by extracting meaningful features from input audio frames. Such features, however, may consist not only of information a... |
Title: Transformer with Memory Replay Abstract: Transformers achieve state-of-the-art performance for natural language processing tasks by pre-training on large-scale text corpora. They are extremely compute-intensive and have very high sample complexity. Memory replay is a mechanism that remembers and reuses past exam... |
Title: Service Delay Minimization for Federated Learning over Mobile Devices Abstract: Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices. Unlike ... |
Title: Automated Scoring for Reading Comprehension via In-context BERT Tuning Abstract: Automated scoring of open-ended student responses has the potential to significantly reduce human grader effort. Recent advances in automated scoring often leverage textual representations based on pre-trained language models such a... |
Title: Recurrent segmentation meets block models in temporal networks Abstract: A popular approach to model interactions is to represent them as a network with nodes being the agents and the interactions being the edges. Interactions are often timestamped, which leads to having timestamped edges. Many real-world tempor... |
Title: Mean-Field Analysis of Two-Layer Neural Networks: Global Optimality with Linear Convergence Rates Abstract: We consider optimizing two-layer neural networks in the mean-field regime where the learning dynamics of network weights can be approximated by the evolution in the space of probability measures over the w... |
Title: MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation Abstract: Video prediction is a challenging task. The quality of video frames from current state-of-the-art (SOTA) generative models tends to be poor and generalization beyond the training data is difficult. Furthermore, exist... |
Title: Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes Abstract: Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. Recently, dynamic treatment regime (DTR) with reinforcement learn... |
Title: Confident Clustering via PCA Compression Ratio and Its Application to Single-cell RNA-seq Analysis Abstract: Unsupervised clustering algorithms for vectors has been widely used in the area of machine learning. Many applications, including the biological data we studied in this paper, contain some boundary datapo... |
Title: A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities Abstract: Recent advances in Neural Architecture Search (NAS) such as one-shot NAS offer the ability to extract specialized hardware-aware sub-network configurations from a task-specific super-network. While considerable eff... |
Title: Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease Abstract: Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work, we investigate the use of T... |
Title: A toolbox for idea generation and evaluation: Machine learning, data-driven, and contest-driven approaches to support idea generation Abstract: The significance and abundance of data are increasing due to the growing digital data generated from social media, sensors, scholarly literature, patents, different form... |
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