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Title: Finite-Time Analysis of Fully Decentralized Single-Timescale Actor-Critic Abstract: Decentralized Actor-Critic (AC) algorithms have been widely utilized for multi-agent reinforcement learning (MARL) and have achieved remarkable success. Apart from its empirical success, the theoretical convergence property of de... |
Title: Darknet Traffic Classification and Adversarial Attacks Abstract: The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detection of darknet traffic in an attempt to block these criminal activitie... |
Title: Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading Abstract: The computationally expensive estimation of engineering demand parameters (EDPs) via finite element (FE) models, while considering earthquake and parameter uncertainty limits th... |
Title: Don't "research fast and break things": On the ethics of Computational Social Science Abstract: This article is concerned with setting up practical guardrails within the research activities and environments of CSS. It aims to provide CSS scholars, as well as policymakers and other stakeholders who apply CSS meth... |
Title: PAC-Net: A Model Pruning Approach to Inductive Transfer Learning Abstract: Inductive transfer learning aims to learn from a small amount of training data for the target task by utilizing a pre-trained model from the source task. Most strategies that involve large-scale deep learning models adopt initialization w... |
Title: A Functional Information Perspective on Model Interpretation Abstract: Contemporary predictive models are hard to interpret as their deep nets exploit numerous complex relations between input elements. This work suggests a theoretical framework for model interpretability by measuring the contribution of relevant... |
Title: tBDFS: Temporal Graph Neural Network Leveraging DFS Abstract: Temporal graph neural networks (temporal GNNs) have been widely researched, reaching state-of-the-art results on multiple prediction tasks. A common approach employed by most previous works is to apply a layer that aggregates information from the hist... |
Title: Balancing Bias and Variance for Active Weakly Supervised Learning Abstract: As a widely used weakly supervised learning scheme, modern multiple instance learning (MIL) models achieve competitive performance at the bag level. However, instance-level prediction, which is essential for many important applications, ... |
Title: A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning Abstract: An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Deep/machine learning (ML/DL) has been hugely leveraged to solve complex problems invol... |
Title: Universality and approximation bounds for echo state networks with random weights Abstract: We study the uniform approximation of echo state networks with randomly generated internal weights. These models, in which only the readout weights are optimized during training, have made empirical success in learning dy... |
Title: Federated Learning on Riemannian Manifolds Abstract: Federated learning (FL) has found many important applications in smart-phone-APP based machine learning applications. Although many algorithms have been studied for FL, to the best of our knowledge, algorithms for FL with nonconvex constraints have not been st... |
Title: An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines Abstract: Adversarial perturbations have drawn great attentions in various machine learning models. In this paper, we investigate the sample adversarial perturbations for nonlinear support vector machines (SVMs). D... |
Title: Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations Abstract: Neural network based data-driven operator learning schemes have shown tremendous potential in computational mechanics. DeepONet is one such neural network architecture which has gained widesprea... |
Title: Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies Abstract: In this paper, we present a novel Heavy-Tailed Stochastic Policy Gradient (HT-PSG) algorithm to deal with the challenges of sparse rewards in continuous control problems. Sparse reward is common in continuous control r... |
Title: DeepEmotex: Classifying Emotion in Text Messages using Deep Transfer Learning Abstract: Transfer learning has been widely used in natural language processing through deep pretrained language models, such as Bidirectional Encoder Representations from Transformers and Universal Sentence Encoder. Despite the great ... |
Title: Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks Abstract: Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of supervised learning applications. However, accurately quantifying the uncertainty in DNN predictions remains ... |
Title: An Unsupervised Deep-Learning Method for Bone Age Assessment Abstract: The bone age, reflecting the degree of development of the bones, can be used to predict the adult height and detect endocrine diseases of children. Both examinations of radiologists and variability of operators have a significant impact on bo... |
Title: Mathematical Theory of Bayesian Statistics for Unknown Information Source Abstract: In statistical inference, uncertainty is unknown and all models are wrong. A person who makes a statistical model and a prior distribution is simultaneously aware that they are fictional and virtual candidates. In order to study ... |
Title: Physics-driven Deep Learning for PET/MRI Abstract: In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer, neurological disorders, ... |
Title: Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology Abstract: Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large ... |
Title: Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Review Abstract: There is a necessity to develop affordable, and reliable diagnostic tools, which allow containing the COVID-19 spreading. Machine Learning (ML) algorithms have been proposed to design support decision-makin... |
Title: Gradient Boosting Performs Low-Rank Gaussian Process Inference Abstract: This paper shows that gradient boosting based on symmetric decision trees can be equivalently reformulated as a kernel method that converges to the solution of a certain Kernel Ridgeless Regression problem. Thus, for low-rank kernels, we ob... |
Title: A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation Abstract: The goal of model compression is to reduce the size of a large neural network while retaining a comparable performance. As a result, computation and memory costs in resource-limited applications may be significan... |
Title: RadNet: Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting Abstract: Efficient and accurate incident prediction in spatio-temporal systems is critical to minimize service downtime and optimize performance. This work aims to utilize historic data to predict and diagnose incidents... |
Title: Federated Offline Reinforcement Learning Abstract: Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are available across medical institutions, they are prohibited from shar... |
Title: Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Machine Learning Abstract: This work revisits the joint beamforming (BF) and antenna selection (AS) problem, as well as its robust beamforming (RBF) version under imperfect channel state information (CSI). Such problems arise... |
Title: MammoDL: Mammographic Breast Density Estimation using Federated Learning Abstract: Assessing breast cancer risk from imaging remains a subjective process, in which radiologists employ computer aided detection (CAD) systems or qualitative visual assessment to estimate breast percent density (PD). More advanced ma... |
Title: NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks Abstract: Membership inference attacks (MIAs) against machine learning models can lead to serious privacy risks for the training dataset used in the model training. In this paper, we propose a novel and effective Neuron-Guided Defen... |
Title: gDDIM: Generalized denoising diffusion implicit models Abstract: Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~(DMs). Instead of constructing a non-Markov noising process as in the original DDIM paper, we examine the mechanism of DDIM from a numerical perspective... |
Title: Parameter Convex Neural Networks Abstract: Deep learning utilizing deep neural networks (DNNs) has achieved a lot of success recently in many important areas such as computer vision, natural language processing, and recommendation systems. The lack of convexity for DNNs has been seen as a major disadvantage of m... |
Title: Communication-Efficient Robust Federated Learning with Noisy Labels Abstract: Federated learning (FL) is a promising privacy-preserving machine learning paradigm over distributed located data. In FL, the data is kept locally by each user. This protects the user privacy, but also makes the server difficult to ver... |
Title: PhML-DyR: A Physics-Informed ML framework for Dynamic Reconfiguration in Power Systems Abstract: A transformation of the US electricity sector is underway with aggressive targets to achieve 100% carbon pollution-free electricity by 2035. To achieve this objective while maintaining a safe and reliable power grid,... |
Title: A Simplified Un-Supervised Learning Based Approach for Ink Mismatch Detection in Handwritten Hyper-Spectral Document Images Abstract: Hyper-spectral imaging has become the latest trend in the field of optical imaging systems. Among various other applications, hyper-spectral imaging has been widely used for analy... |
Title: Rare event failure test case generation in Learning-Enabled-Controllers Abstract: Machine learning models have prevalent applications in many real-world problems, which increases the importance of correctness in the behaviour of these trained models. Finding a good test case that can reveal the potential failure... |
Title: Memorization-Dilation: Modeling Neural Collapse Under Noise Abstract: The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems. During the terminal phase of training a deep neural network, the feature embedding of all ... |
Title: Model-based Offline Imitation Learning with Non-expert Data Abstract: Although Behavioral Cloning (BC) in theory suffers compounding errors, its scalability and simplicity still makes it an attractive imitation learning algorithm. In contrast, imitation approaches with adversarial training typically does not sha... |
Title: Defending Adversarial Examples by Negative Correlation Ensemble Abstract: The security issues in DNNs, such as adversarial examples, have attracted much attention. Adversarial examples refer to the examples which are capable to induce the DNNs return completely predictions by introducing carefully designed pertu... |
Title: Federated Learning with GAN-based Data Synthesis for Non-IID Clients Abstract: Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative learning paradigm. However, it suffers from the non-independent and identically distributed (non-IID) data among clients. In this paper, we pro... |
Title: Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks Abstract: For learning graph representations, not all detailed structures within a graph are relevant to the given graph tasks. Task-relevant structures can be $localized$ or $sparse$ which are only involved in subgraphs or characterized by t... |
Title: Learning to Generate Levels by Imitating Evolution Abstract: Search-based procedural content generation (PCG) is a well-known method used for level generation in games. Its key advantage is that it is generic and able to satisfy functional constraints. However, due to the heavy computational costs to run these a... |
Title: DRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting Abstract: Time-series forecasting plays an important role in many real-world scenarios, such as equipment life cycle forecasting, weather forecasting, and traffic flow forecasting. It can be observed from recent research th... |
Title: Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound Abstract: Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively. In this paper, we focus on both discover... |
Title: Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena Abstract: Interpretable machine learning (IML) is concerned with the behavior and the properties of machine learning models. Scientists, however, are only interested in the model as a gateway to underst... |
Title: Bilateral Dependency Optimization: Defending Against Model-inversion Attacks Abstract: Through using only a well-trained classifier, model-inversion (MI) attacks can recover the data used for training the classifier, leading to the privacy leakage of the training data. To defend against MI attacks, previous work... |
Title: Monitoring and Proactive Management of QoS Levels in Pervasive Applications Abstract: The advent of Edge Computing (EC) as a promising paradigm that provides multiple computation and analytics capabilities close to data sources opens new pathways for novel applications. Nonetheless, the limited computational cap... |
Title: Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd Counting Abstract: The lightweight crowd counting models, in particular knowledge distillation (KD) based models, have attracted rising attention in recent years due to their superiority on computational efficiency and hardware requi... |
Title: Svadhyaya system for the Second Diagnosing COVID-19 using Acoustics Challenge 2021 Abstract: This report describes the system used for detecting COVID-19 positives using three different acoustic modalities, namely speech, breathing, and cough in the second DiCOVA challenge. The proposed system is based on the co... |
Title: Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection Abstract: This paper aims to develop an acoustic signal-based unsupervised anomaly detection method for automatic machine monitoring. Existing approaches such as deep autoencoder (DAE), variational autoencoder (VAE), conditional va... |
Title: A General framework for PAC-Bayes Bounds for Meta-Learning Abstract: Meta learning automatically infers an inductive bias, that includes the hyperparameter of the base-learning algorithm, by observing data from a finite number of related tasks. This paper studies PAC-Bayes bounds on meta generalization gap. The ... |
Title: Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution Abstract: Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves. This makes it difficult to understand the decision process which lead to the selected ... |
Title: A Dataset and Benchmark for Automatically Answering and Generating Machine Learning Final Exams Abstract: Can a machine learn machine learning? We propose to answer this question using the same criteria we use to answer a similar question: can a human learn machine learning? We automatically answer MIT final exa... |
Title: ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition Abstract: Neural message passing is a basic feature extraction unit for graph-structured data that takes account of the impact of neighboring node features in network propagation from one layer to the next. We model such pr... |
Title: Learned reconstruction with convergence guarantees Abstract: In recent years, deep learning has achieved remarkable empirical success for image reconstruction. This has catalyzed an ongoing quest for precise characterization of correctness and reliability of data-driven methods in critical use-cases, for instanc... |
Title: Semi-Supervised Hierarchical Graph Classification Abstract: Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph usually represents a real-world entity, e.g., a user in a social network, ... |
Title: Multi-instrument Music Synthesis with Spectrogram Diffusion Abstract: An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes. Recent neural synthesizers have exhibited a tradeoff between domain-specific ... |
Title: Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency Abstract: Federated learning (FL) is a distributed machine learning approach where multiple clients collaboratively train a joint model without exchanging their data. Despite FL's unprecedented success in dat... |
Title: Squeeze All: Novel Estimator and Self-Normalized Bound for Linear Contextual Bandits Abstract: We propose a novel algorithm for linear contextual bandits with $O(\sqrt{dT \log T})$ regret bound, where $d$ is the dimension of contexts and $T$ is the time horizon. Our proposed algorithm is equipped with a novel es... |
Title: Feature Selection using e-values Abstract: In the context of supervised parametric models, we introduce the concept of e-values. An e-value is a scalar quantity that represents the proximity of the sampling distribution of parameter estimates in a model trained on a subset of features to that of the model traine... |
Title: Learning Imbalanced Datasets with Maximum Margin Loss Abstract: A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones. That is, underfitting for minority... |
Title: Fast building segmentation from satellite imagery and few local labels Abstract: Innovations in computer vision algorithms for satellite image analysis can enable us to explore global challenges such as urbanization and land use change at the planetary level. However, domain shift problems are a common occurrenc... |
Title: An application of neural networks to a problem in knot theory and group theory (untangling braids) Abstract: We report on our success on solving the problem of untangling braids up to length 20 and width 4. We use feed-forward neural networks in the framework of reinforcement learning to train the agent to choos... |
Title: Object Detection, Recognition, Deep Learning, and the Universal Law of Generalization Abstract: Object detection and recognition are fundamental functions underlying the success of species. Because the appearance of an object exhibits a large variability, the brain has to group these different stimuli under the ... |
Title: Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning Abstract: We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as proportional fairness (smooth conc... |
Title: Cross-TOP: Zero-Shot Cross-Schema Task-Oriented Parsing Abstract: Deep learning methods have enabled task-oriented semantic parsing of increasingly complex utterances. However, a single model is still typically trained and deployed for each task separately, requiring labeled training data for each, which makes i... |
Title: Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks Abstract: In recent years, graph neural networks (GNNs) have achieved state-of-the-art performance for node classification. However, most existing GNNs would suffer from the graph imbalance problem. In many real-world scenarios... |
Title: Memory Classifiers: Two-stage Classification for Robustness in Machine Learning Abstract: The performance of machine learning models can significantly degrade under distribution shifts of the data. We propose a new method for classification which can improve robustness to distribution shifts, by combining expert... |
Title: Intrinsic dimensionality and generalization properties of the $\mathcal{R}$-norm inductive bias Abstract: We study the structural and statistical properties of $\mathcal{R}$-norm minimizing interpolants of datasets labeled by specific target functions. The $\mathcal{R}$-norm is the basis of an inductive bias for... |
Title: Large-Scale Retrieval for Reinforcement Learning Abstract: Effective decision making involves flexibly relating past experiences and relevant contextual information to a novel situation. In deep reinforcement learning, the dominant paradigm is for an agent to amortise information that helps decision-making into ... |
Title: Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels? Abstract: We investigate whether self-supervised learning (SSL) can improve online reinforcement learning (RL) from pixels. We extend the contrastive reinforcement learning framework (e.g., CURL) that jointly optimizes SSL and RL lo... |
Title: Tight Bounds for State Tomography with Incoherent Measurements Abstract: We consider the classic question of state tomography: given copies of an unknown quantum state $\rho\in\mathbb{C}^{d\times d}$, output $\widehat{\rho}$ for which $\|\rho - \widehat{\rho}\|_{\mathsf{tr}} \le \varepsilon$. When one is allowed... |
Title: Causal Balancing for Domain Generalization Abstract: While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations. While current domain generalizati... |
Title: Meta Optimal Transport Abstract: We study the use of amortized optimization to predict optimal transport (OT) maps from the input measures, which we call Meta OT. This helps repeatedly solve similar OT problems between different measures by leveraging the knowledge and information present from past problems to r... |
Title: Balanced Product of Experts for Long-Tailed Recognition Abstract: Many real-world recognition problems suffer from an imbalanced or long-tailed label distribution. Those distributions make representation learning more challenging due to limited generalization over the tail classes. If the test distribution diffe... |
Title: Is Self-Supervised Learning More Robust Than Supervised Learning? Abstract: Self-supervised contrastive learning is a powerful tool to learn visual representation without labels. Prior work has primarily focused on evaluating the recognition accuracy of various pre-training algorithms, but has overlooked other b... |
Title: Interactively Learning Preference Constraints in Linear Bandits Abstract: We study sequential decision-making with known rewards and unknown constraints, motivated by situations where the constraints represent expensive-to-evaluate human preferences, such as safe and comfortable driving behavior. We formalize th... |
Title: Rethinking Spatial Invariance of Convolutional Networks for Object Counting Abstract: Previous work generally believes that improving the spatial invariance of convolutional networks is the key to object counting. However, after verifying several mainstream counting networks, we surprisingly found too strict pix... |
Title: Accelerated Algorithms for Monotone Inclusions and Constrained Nonconvex-Nonconcave Min-Max Optimization Abstract: We study monotone inclusions and monotone variational inequalities, as well as their generalizations to non-monotone settings. We first show that the Extra Anchored Gradient (EAG) algorithm, origina... |
Title: List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering Abstract: We study the problem of list-decodable sparse mean estimation. Specifically, for a parameter $\alpha \in (0, 1/2)$, we are given $m$ points in $\mathbb{R}^n$, $\lfloor \alpha m \rfloor$ of which are i.i.d. samples from a distributi... |
Title: ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning Abstract: Real-Time Bidding (RTB) is an important mechanism in modern online advertising systems. Advertisers employ bidding strategies in RTB to optimize their advertising effects subject to various financial requirements, especially ... |
Title: StructCoder: Structure-Aware Transformer for Code Generation Abstract: There has been a recent surge of interest in automating software engineering tasks using deep learning. This work addresses the problem of code generation where the goal is to generate target code given source code in a different language or ... |
Title: Measuring the Carbon Intensity of AI in Cloud Instances Abstract: By providing unprecedented access to computational resources, cloud computing has enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a commensurate carbon footprint. As a ... |
Title: ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences Abstract: Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike machine-made time series, these action sequences are highly disparate as the time taken to finish a similar a... |
Title: A new distance measurement and its application in K-Means Algorithm Abstract: K-Means clustering algorithm is one of the most commonly used clustering algorithms because of its simplicity and efficiency. K-Means clustering algorithm based on Euclidean distance only pays attention to the linear distance between s... |
Title: Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes Abstract: Within Process mining, discovery techniques had made it possible to construct business process models automatically from event logs. However, results often do not achieve... |
Title: Hierarchical Federated Learning with Privacy Abstract: Federated learning (FL), where data remains at the federated clients, and where only gradient updates are shared with a central aggregator, was assumed to be private. Recent work demonstrates that adversaries with gradient-level access can mount successful i... |
Title: Dynamic mean field programming Abstract: A dynamic mean field theory is developed for model based Bayesian reinforcement learning in the large state space limit. In an analogy with the statistical physics of disordered systems, the transition probabilities are interpreted as couplings, and value functions as det... |
Title: Bayesian Estimation of Differential Privacy Abstract: Algorithms such as Differentially Private SGD enable training machine learning models with formal privacy guarantees. However, there is a discrepancy between the protection that such algorithms guarantee in theory and the protection they afford in practice. A... |
Title: Learning the Space of Deep Models Abstract: Embedding of large but redundant data, such as images or text, in a hierarchy of lower-dimensional spaces is one of the key features of representation learning approaches, which nowadays provide state-of-the-art solutions to problems once believed hard or impossible to... |
Title: On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond Abstract: The FedProx algorithm is a simple yet powerful distributed proximal point optimization method widely used for federated learning (FL) over heterogeneous data. Despite its popularity and remarkable success witness... |
Title: GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions Abstract: We develop data-driven methods incorporating geometric and topological information to learn parsimonious representations of nonlinear dynamics from observations. We develop approaches for learni... |
Title: Localized adversarial artifacts for compressed sensing MRI Abstract: As interest in deep neural networks (DNNs) for image reconstruction tasks grows, their reliability has been called into question (Antun et al., 2020; Gottschling et al., 2020). However, recent work has shown that compared to total variation (TV... |
Title: Human-AI Interaction Design in Machine Teaching Abstract: Machine Teaching (MT) is an interactive process where a human and a machine interact with the goal of training a machine learning model (ML) for a specified task. The human teacher communicates their task expertise and the machine student gathers the requ... |
Title: Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift Abstract: We introduce LIMES, a new method for learning with non-stationary streaming data, inspired by the recent success of meta-learning. The main idea is not to attempt to learn a single classifier that would have to work ... |
Title: How Much is Enough? A Study on Diffusion Times in Score-based Generative Models Abstract: Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data. While recent works have started to lay down a theoretical foundation f... |
Title: Multifidelity Reinforcement Learning with Control Variates Abstract: In many computational science and engineering applications, the output of a system of interest corresponding to a given input can be queried at different levels of fidelity with different costs. Typically, low-fidelity data is cheap and abundan... |
Title: An Image Processing Pipeline for Camera Trap Time-Lapse Recordings Abstract: A new open-source image processing pipeline for analyzing camera trap time-lapse recordings is described. This pipeline includes machine learning models to assist human-in-the-loop video segmentation and animal re-identification. We pre... |
Title: MEAT: Maneuver Extraction from Agent Trajectories Abstract: Advances in learning-based trajectory prediction are enabled by large-scale datasets. However, in-depth analysis of such datasets is limited. Moreover, the evaluation of prediction models is limited to metrics averaged over all samples in the dataset. W... |
Title: Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification Abstract: Deep learning models have shown their potential for several applications. However, most of the models are opaque and difficult to trust due to their complex reasoning -... |
Title: Fast Deep Autoencoder for Federated learning Abstract: This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep Autoencoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network in a non-iterative way, which drastically redu... |
Title: Distributionally Robust End-to-End Portfolio Construction Abstract: We propose an end-to-end distributionally robust system for portfolio construction that integrates the asset return prediction model with a distributionally robust portfolio optimization model. We also show how to learn the risk-tolerance parame... |
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