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Title: Evaluating histopathology transfer learning with ChampKit Abstract: Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, including immune cell detection ... |
Title: When adversarial attacks become interpretable counterfactual explanations Abstract: We argue that, when learning a 1-Lipschitz neural network with the dual loss of an optimal transportation problem, the gradient of the model is both the direction of the transportation plan and the direction to the closest advers... |
Title: On the Finite-Time Performance of the Knowledge Gradient Algorithm Abstract: The knowledge gradient (KG) algorithm is a popular and effective algorithm for the best arm identification (BAI) problem. Due to the complex calculation of KG, theoretical analysis of this algorithm is difficult, and existing results ar... |
Title: Robust Reinforcement Learning with Distributional Risk-averse formulation Abstract: Robust Reinforcement Learning tries to make predictions more robust to changes in the dynamics or rewards of the system. This problem is particularly important when the dynamics and rewards of the environment are estimated from t... |
Title: Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception Abstract: Data-driven models (DDM) based on machine learning and other AI techniques play an important role in the perception of increasingly autonomous systems. Due to the merely implicit definition of th... |
Title: Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization Abstract: Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied in practice but their performance suffers from the high variance of the gradient estimate. Several procedures were proposed to r... |
Title: Tailored max-out networks for learning convex PWQ functions Abstract: Convex piecewise quadratic (PWQ) functions frequently appear in control and elsewhere. For instance, it is well-known that the optimal value function (OVF) as well as Q-functions for linear MPC are convex PWQ functions. Now, in learning-based ... |
Title: Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring Abstract: Attributes skew hinders the current federated learning (FL) frameworks from consistent optimization directions among the clients, which inevitably leads to performance reduction and unstabl... |
Title: Physics-Informed Transfer Learning Strategy to Accelerate Unsteady Fluid Flow Simulations Abstract: Since the derivation of the Navier Stokes equations, it has become possible to numerically solve real world viscous flow problems (computational fluid dynamics (CFD)). However, despite the rapid advancements in th... |
Title: Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites Abstract: In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the storag... |
Title: Adversarial Audio Synthesis with Complex-valued Polynomial Networks Abstract: Time-frequency (TF) representations in audio synthesis have been increasingly modeled with real-valued networks. However, overlooking the complex-valued nature of TF representations can result in suboptimal performance and require addi... |
Title: Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds Abstract: This paper considers the multi-armed bandit (MAB) problem and provides a new best-of-both-worlds (BOBW) algorithm that works nearly optimally in both stochastic and adversarial settings. In stochastic settings, some... |
Title: Reconstructing vehicles from orthographic drawings using deep neural networks Abstract: This paper explores the current state-of-the-art of object reconstruction from multiple orthographic drawings using deep neural networks. It proposes two algorithms to extract multiple views from a single image. The paper pro... |
Title: The Dynamics of Riemannian Robbins-Monro Algorithms Abstract: Many important learning algorithms, such as stochastic gradient methods, are often deployed to solve nonlinear problems on Riemannian manifolds. Motivated by these applications, we propose a family of Riemannian algorithms generalizing and extending t... |
Title: Classification of ECG based on Hybrid Features using CNNs for Wearable Applications Abstract: Sudden cardiac death and arrhythmia account for a large percentage of all deaths worldwide. Electrocardiography (ECG) is the most widely used screening tool for cardiovascular diseases. Traditionally, ECG signals are cl... |
Title: Supervised Dictionary Learning with Auxiliary Covariates Abstract: Supervised dictionary learning (SDL) is a classical machine learning method that simultaneously seeks feature extraction and classification tasks, which are not necessarily a priori aligned objectives. The goal of SDL is to learn a class-discrimi... |
Title: Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks Abstract: Deep neural networks (DNN) are a promising tool in medical applications. However, the implementation of complex DNNs on battery-powered devices is challenging due to high energy costs for communication. In th... |
Title: Universally Expressive Communication in Multi-Agent Reinforcement Learning Abstract: Allowing agents to share information through communication is crucial for solving complex tasks in multi-agent reinforcement learning. In this work, we consider the question of whether a given communication protocol can express ... |
Title: RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search Abstract: Social bots are referred to as the automated accounts on social networks that make attempts to behave like human. While Graph Neural Networks (GNNs) has been massively applied to the field of social bot detect... |
Title: Counting Markov Equivalent Directed Acyclic Graphs Consistent with Background Knowledge Abstract: A polynomial-time exact algorithm for counting the number of directed acyclic graphs in a Markov equivalence class was recently given by Wien\"obst, Bannach, and Li\'skiewicz (AAAI 2021). In this paper, we consider ... |
Title: Adversarial Vulnerability of Randomized Ensembles Abstract: Despite the tremendous success of deep neural networks across various tasks, their vulnerability to imperceptible adversarial perturbations has hindered their deployment in the real world. Recently, works on randomized ensembles have empirically demonst... |
Title: Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images Abstract: Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. If the motion artefacts alter a cor... |
Title: Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning Abstract: This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect accuracy? Positive evidence is provided for the first t... |
Title: Deep Variational Implicit Processes Abstract: Implicit processes (IPs) are a generalization of Gaussian processes (GPs). IPs may lack a closed-form expression but are easy to sample from. Examples include, among others, Bayesian neural networks or neural samplers. IPs can be used as priors over functions, result... |
Title: Stein Variational Goal Generation For Reinforcement Learning in Hard Exploration Problems Abstract: Multi-goal Reinforcement Learning has recently attracted a large amount of research interest. By allowing experience to be shared between related training tasks, this setting favors generalization for new tasks at... |
Title: Visual Radial Basis Q-Network Abstract: While reinforcement learning (RL) from raw images has been largely investigated in the last decade, existing approaches still suffer from a number of constraints. The high input dimension is often handled using either expert knowledge to extract handcrafted features or env... |
Title: Conformal Off-Policy Prediction Abstract: Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment. Most existing methods focus on the expected return, define the target parameter through averaging and provide a point estimator only. I... |
Title: Task Transfer and Domain Adaptation for Zero-Shot Question Answering Abstract: Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks. However, when applying machine learning methods to new domains, labeled data may not always be avail... |
Title: CNN-based Classification Framework for Lung Tissues with Auxiliary Information Abstract: Interstitial lung diseases are a large group of heterogeneous diseases characterized by different degrees of alveolitis and pulmonary fibrosis. Accurately diagnosing these diseases has significant guiding value for formulati... |
Title: Generalizing experimental findings: identification beyond adjustments Abstract: We aim to generalize the results of a randomized controlled trial (RCT) to a target population with the help of some observational data. This is a problem of causal effect identification with multiple data sources. Challenges arise w... |
Title: COVIDHunter: COVID-19 pandemic wave prediction and mitigation via seasonality-aware modeling Abstract: Early detection and isolation of COVID-19 patients are essential for successful implementation of mitigation strategies and eventually curbing the disease spread. With a limited number of daily COVID-19 tests p... |
Title: Bandwidth Enables Generalization in Quantum Kernel Models Abstract: Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings. For example, quantum kernel methods have been shown to provide an exponential speedup on a learning version of... |
Title: Causal Discovery for Fairness Abstract: It is crucial to consider the social and ethical consequences of AI and ML based decisions for the safe and acceptable use of these emerging technologies. Fairness, in particular, guarantees that the ML decisions do not result in discrimination against individuals or minor... |
Title: Exploring speaker enrolment for few-shot personalisation in emotional vocalisation prediction Abstract: In this work, we explore a novel few-shot personalisation architecture for emotional vocalisation prediction. The core contribution is an `enrolment' encoder which utilises two unlabelled samples of the target... |
Title: Matching Pursuit Based Scheduling for Over-the-Air Federated Learning Abstract: This paper develops a class of low-complexity device scheduling algorithms for over-the-air federated learning via the method of matching pursuit. The proposed scheme tracks closely the close-to-optimal performance achieved by differ... |
Title: Severe Damage Recovery in Evolving Soft Robots through Differentiable Programming Abstract: Biological systems are very robust to morphological damage, but artificial systems (robots) are currently not. In this paper we present a system based on neural cellular automata, in which locomoting robots are evolved an... |
Title: Energy Flows: Towards Determinant-Free Training of Normalizing Flows Abstract: Normalizing flows are a popular approach for constructing probabilistic and generative models. However, maximum likelihood training of flows is challenging due to the need to calculate computationally expensive determinants of Jacobia... |
Title: Learning Best Combination for Efficient N:M Sparsity Abstract: By forcing at most N out of M consecutive weights to be non-zero, the recent N:M network sparsity has received increasing attention for its two attractive advantages: 1) Promising performance at a high sparsity. 2) Significant speedups on NVIDIA A100... |
Title: SoTeacher: A Student-oriented Teacher Network Training Framework for Knowledge Distillation Abstract: How to train an ideal teacher for knowledge distillation is still an open problem. It has been widely observed that a teacher minimizing the empirical risk not necessarily yields the best performing student, sug... |
Title: The Open Kidney Ultrasound Data Set Abstract: Ultrasound use is because of its low cost, non-ionizing, and non-invasive characteristics, and has established itself as a cornerstone radiological examination. Research on ultrasound applications has also expanded, especially with image analysis with machine learnin... |
Title: The Kidneys Are Not All Normal: Investigating the Speckle Distributions of Transplanted Kidneys Abstract: Modelling ultrasound speckle has generated considerable interest for its ability to characterize tissue properties. As speckle is dependent on the underlying tissue architecture, modelling it may aid in task... |
Title: SpecNet2: Orthogonalization-free spectral embedding by neural networks Abstract: Spectral methods which represent data points by eigenvectors of kernel matrices or graph Laplacian matrices have been a primary tool in unsupervised data analysis. In many application scenarios, parametrizing the spectral embedding ... |
Title: Confidence Score for Source-Free Unsupervised Domain Adaptation Abstract: Source-free unsupervised domain adaptation (SFUDA) aims to obtain high performance in the unlabeled target domain using the pre-trained source model, not the source data. Existing SFUDA methods assign the same importance to all target samp... |
Title: Explainable AI for High Energy Physics Abstract: Neural Networks are ubiquitous in high energy physics research. However, these highly nonlinear parameterized functions are treated as \textit{black boxes}- whose inner workings to convey information and build the desired input-output relationship are often intrac... |
Title: Transformers are Meta-Reinforcement Learners Abstract: The transformer architecture and variants presented remarkable success across many machine learning tasks in recent years. This success is intrinsically related to the capability of handling long sequences and the presence of context-dependent weights from t... |
Title: Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model Abstract: Natural Language Processing(NLP) demonstrates a great potential to support financial decision-making by analyzing the text from social media or news outlets. In this work, we build a platform to study the NLP... |
Title: Deep Isolation Forest for Anomaly Detection Abstract: Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years. It iteratively performs axis-parallel data space partition in a tree structure to isolate deviated data objects from the other data, with the isolation... |
Title: CorticalFlow$^{++}$: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability Abstract: The problem of Cortical Surface Reconstruction from magnetic resonance imaging has been traditionally addressed using lengthy pipelines of image processing techniques like FreeSurfer, CAT, or CIVET.... |
Title: Permutation Search of Tensor Network Structures via Local Sampling Abstract: Recent works put much effort into tensor network structure search (TN-SS), aiming to select suitable tensor network (TN) structures, involving the TN-ranks, formats, and so on, for the decomposition or learning tasks. In this paper, we ... |
Title: On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis Abstract: Nonlinear independent component analysis (nICA) aims at recovering statistically independent latent components that are mixed by unknown nonlinear functions. Central to nICA is the identifiability of... |
Title: Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training Abstract: The successful emergence of deep learning (DL) in wireless system applications has raised concerns about new security-related challenges. One such security challenge is adversarial attacks. Although there has ... |
Title: Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search Abstract: Improving the quality of search results can significantly enhance users experience and engagement with search engines. In spite of several recent advancements in the fields of machine learning and data mining, correctly... |
Title: Learning Enhanced Representations for Tabular Data via Neighborhood Propagation Abstract: Prediction over tabular data is an essential and fundamental problem in many important downstream tasks. However, existing methods either take a data instance of the table independently as input or do not fully utilize the ... |
Title: Probabilistic Conformal Prediction Using Conditional Random Samples Abstract: This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set. Given inputs, PCP construct the predictive set based on random samples f... |
Title: Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data Abstract: High-fidelity reconstruction of fluids from sparse multiview RGB videos remains a formidable challenge due to the complexity of the underlying physics as well as complex occlusion and lighting in captures. Existing solutions eithe... |
Title: LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks Abstract: Fine-tuning pretrained language models (LMs) without making any architectural changes has become a norm for learning various language downstream tasks. However, for non-language downstream tasks, a common practice is to emplo... |
Title: Zeroth-Order Topological Insights into Iterative Magnitude Pruning Abstract: Modern-day neural networks are famously large, yet also highly redundant and compressible; there exist numerous pruning strategies in the deep learning literature that yield over 90% sparser sub-networks of fully-trained, dense architec... |
Title: FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks Abstract: Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teacher GNN into a shallower student GNN. However, it is ... |
Title: Resolution Limits of Non-Adaptive 20 Questions Search for a Moving Target Abstract: Using the 20 questions estimation framework with query-dependent noise, we study non-adaptive search strategies for a moving target over the unit cube with unknown initial location and velocities under a piecewise constant veloci... |
Title: Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory Abstract: We present a motion planning algorithm for a class of uncertain control-affine nonlinear systems which guarantees runtime safety and goal reachability when using high-dimensional sensor measurements (... |
Title: Embarrassingly Parallel Independent Training of Multi-Layer Perceptrons with Heterogeneous Architectures Abstract: The definition of a Neural Network architecture is one of the most critical and challenging tasks to perform. In this paper, we propose ParallelMLPs. ParallelMLPs is a procedure to enable the traini... |
Title: A theory of learning with constrained weight-distribution Abstract: A central question in computational neuroscience is how structure determines function in neural networks. The emerging high-quality large-scale connectomic datasets raise the question of what general functional principles can be gleaned from str... |
Title: A Stochastic Proximal Method for Nonsmooth Regularized Finite Sum Optimization Abstract: We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient sub-structure. Our regularizer is only assumed to be lower semi-continuous and prox-bounded. We combi... |
Title: Overparametrized linear dimensionality reductions: From projection pursuit to two-layer neural networks Abstract: Given a cloud of $n$ data points in $\mathbb{R}^d$, consider all projections onto $m$-dimensional subspaces of $\mathbb{R}^d$ and, for each such projection, the empirical distribution of the projecte... |
Title: Fiberwise dimensionality reduction of topologically complex data with vector bundles Abstract: Datasets with non-trivial large scale topology can be hard to embed in low-dimensional Euclidean space with existing dimensionality reduction algorithms. We propose to model topologically complex datasets using vector ... |
Title: Generalizable Method for Face Anti-Spoofing with Semi-Supervised Learning Abstract: Face anti-spoofing has drawn a lot of attention due to the high security requirements in biometric authentication systems. Bringing face biometric to commercial hardware became mostly dependent on developing reliable methods for ... |
Title: Machine Learning-Driven Process of Alumina Ceramics Laser Machining Abstract: Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as t... |
Title: Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training Abstract: Data clipping is crucial in reducing noise in quantization operations and improving the achievable accuracy of quantization-aware training (QAT). Current practices rely on heuristics to set clipping threshold ... |
Title: Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations Abstract: Adversarial training (AT) and its variants have spearheaded progress in improving neural network robustness to adversarial perturbations and common corruptions in the las... |
Title: Multimodal Learning with Transformers: A Survey Abstract: Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications and big data, Transformer-based multimodal learning has become a hot topic in A... |
Title: The Modality Focusing Hypothesis: On the Blink of Multimodal Knowledge Distillation Abstract: Multimodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning. One common practice is to adopt a well-performed multimodal network as the teacher in the hope that i... |
Title: Mapping fNIRS to fMRI with Neural Data Augmentation and Machine Learning Models Abstract: Advances in neuroimaging techniques have provided us novel insights into understanding how the human mind works. Functional magnetic resonance imaging (fMRI) is the most popular and widely used neuroimaging technique, and t... |
Title: What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience Abstract: In computational reinforcement learning, a growing body of work seeks to construct an agent's perception of the world through predictions of future sensations; predictions about environment... |
Title: On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice Abstract: We study two of the most popular performance metrics in medical image segmentation, Accuracy and Dice, when the target labels are noisy. For both metrics, several statements rel... |
Title: Robust Distillation for Worst-class Performance Abstract: Knowledge distillation has proven to be an effective technique in improving the performance a student model using predictions from a teacher model. However, recent work has shown that gains in average efficiency are not uniform across subgroups in the dat... |
Title: Explainable Mixed Data Representation and Lossless Visualization Toolkit for Knowledge Discovery Abstract: Developing Machine Learning (ML) algorithms for heterogeneous/mixed data is a longstanding problem. Many ML algorithms are not applicable to mixed data, which include numeric and non-numeric data, text, gra... |
Title: Invariant Structure Learning for Better Generalization and Causal Explainability Abstract: Learning the causal structure behind data is invaluable for improving generalization and obtaining high-quality explanations. We propose a novel framework, Invariant Structure Learning (ISL), that is designed to improve ca... |
Title: Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification Abstract: It is generally believed that the human visual system is biased towards the recognition of shapes rather than textures. This assumption has led to a growing body of work aiming to align deep models' decision-making pr... |
Title: Density Estimation with Autoregressive Bayesian Predictives Abstract: Bayesian methods are a popular choice for statistical inference in small-data regimes due to the regularization effect induced by the prior, which serves to counteract overfitting. In the context of density estimation, the standard Bayesian ap... |
Title: Assessing Privacy Leakage in Synthetic 3-D PET Imaging using Transversal GAN Abstract: Training computer-vision related algorithms on medical images for disease diagnosis or image segmentation is difficult in large part due to privacy concerns. For this reason, generative image models are highly sought after to ... |
Title: Automated Coronary Calcium Scoring using U-Net Models through Semi-supervised Learning on Non-Gated CT Scans Abstract: Every year, thousands of innocent people die due to heart attacks. Often undiagnosed heart attacks can hit people by surprise since many current medical plans don't cover the costs to require th... |
Title: SmartGD: A Self-Challenging Generative Adversarial Network for Graph Drawing Abstract: A multitude of studies have been conducted on graph drawing, but many existing methods only focus on optimizing particular aesthetic aspects of graph layout. Given a graph, generating a good layout that satisfies certain human... |
Title: ReViSe: Remote Vital Signs Measurement Using Smartphone Camera Abstract: Remote Photoplethysmography (rPPG) is a fast, effective, inexpensive and convenient method for collecting biometric data as it enables vital signs estimation using face videos. Remote contactless medical service provisioning has proven to b... |
Title: Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward Abstract: The remarkable success of reinforcement learning (RL) heavily relies on observing the reward of every visited state-action pair. In many real world applications, however, an agent can observe only a score that represents the ... |
Title: Look, Radiate, and Learn: Self-supervised Localisation via Radio-Visual Correspondence Abstract: Next generation cellular networks will implement radio sensing functions alongside customary communications, thereby enabling unprecedented worldwide sensing coverage outdoors. Deep learning has revolutionised comput... |
Title: Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data Abstract: Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle simulations is determined by interatomi... |
Title: GraphMLP: A Graph MLP-Like Architecture for 3D Human Pose Estimation Abstract: Modern multi-layer perceptron (MLP) models have shown competitive results in learning visual representations without self-attention. However, existing MLP models are not good at capturing local details and lack prior knowledge of huma... |
Title: Image-based Treatment Effect Heterogeneity Abstract: Randomized controlled trials (RCTs) are considered the gold standard for estimating the effects of interventions. Recent work has studied effect heterogeneity in RCTs by conditioning estimates on tabular variables such as age and ethnicity. However, such varia... |
Title: Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa Abstract: Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the effect of confounding is well ... |
Title: Compositional Mixture Representations for Vision and Text Abstract: Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture representation imposing the co... |
Title: Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation Abstract: The task of unsupervised semantic segmentation aims to cluster pixels into semantically meaningful groups. Specifically, pixels assigned to the same cluster should share high-level semantic properties like their object or... |
Title: EnergyMatch: Energy-based Pseudo-Labeling for Semi-Supervised Learning Abstract: Recent state-of-the-art methods in semi-supervised learning (SSL) combine consistency regularization with confidence-based pseudo-labeling. To obtain high-quality pseudo-labels, a high confidence threshold is typically adopted. Howe... |
Title: Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process Abstract: In typical scenarios where the Federated Learning (FL) framework applies, it is common for clients to have insufficient training data to produce an accurate model. Thus, models that provide not only point estimations, bu... |
Title: Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets Abstract: Smart manufacturing systems are being deployed at a growing rate because of their ability to interpret a wide variety of sensed information and act on the knowledge gleaned from system observations. In many cases, the ... |
Title: Differentiable and Transportable Structure Learning Abstract: We are interested in unsupervised structure learning with a particular focus on directed acyclic graphical (DAG) models. Compute required to infer these structures is typically super-exponential in the amount of variables, as inference requires a swee... |
Title: Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network Abstract: Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. Traditional non-rigid registration method... |
Title: Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring Abstract: The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use. We distinguish two types of learnable uncertainty: model uncertainty due t... |
Title: Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Abstract: Minimizing the inclusive Kullback-Leibler (KL) divergence with stochastic gradient descent (SGD) is challenging since its gradient is defined as an integral over the posterior. Recently, multiple methods h... |
Title: Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation Abstract: This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track: The No Intera... |
Title: On the reusability of samples in active learning Abstract: An interesting but not extensively studied question in active learning is that of sample reusability: to what extent can samples selected for one learner be reused by another? This paper explains why sample reusability is of practical interest, why reusa... |
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