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Title: $\mathscr{H}$-Consistency Estimation Error of Surrogate Loss Minimizers Abstract: We present a detailed study of estimation errors in terms of surrogate loss estimation errors. We refer to such guarantees as $\mathscr{H}$-consistency estimation error bounds, since they account for the hypothesis set $\mathscr{H}... |
Title: Continual learning on 3D point clouds with random compressed rehearsal Abstract: Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds are important datatype for precise modeling of three-dimensional environmen... |
Title: CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction Abstract: Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine. Recently, promising results have been achieved by leveragi... |
Title: An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models Abstract: Using gradient descent (GD) with fixed or decaying step-size is standard practice in unconstrained optimization problems. However, when the loss function is only locally convex, such a step-size schedule artificially s... |
Title: Deep Apprenticeship Learning for Playing Games Abstract: In the last decade, deep learning has achieved great success in machine learning tasks where the input data is represented with different levels of abstractions. Driven by the recent research in reinforcement learning using deep neural networks, we explore... |
Title: Application of multilayer perceptron with data augmentation in nuclear physics Abstract: Neural networks have become popular in many fields of science since they serve as reliable and powerful tools. Application of the neural networks to the nuclear physics studies has also become popular in recent years because... |
Title: Neural-Symbolic Models for Logical Queries on Knowledge Graphs Abstract: Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic methods traverse a complete knowledge graph to extract the answers, which provides good interpretation... |
Title: Distributed Feature Selection for High-dimensional Additive Models Abstract: Distributed statistical learning is a common strategy for handling massive data where we divide the learning task into multiple local machines and aggregate the results afterward. However, most existing work considers the case where the... |
Title: An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing Abstract: Hyperdimensional Computing (HDC) is a computation framework based on properties of high-dimensional random spaces. It is particularly useful for machine learning in resource-constrained environments, such a... |
Title: Does Crypto Kill? Relationship between Electricity Consumption Carbon Footprints and Bitcoin Transactions Abstract: Cryptocurrencies are gaining more popularity due to their security, making counterfeits impossible. However, these digital currencies have been criticized for creating a large carbon footprint due ... |
Title: Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows Abstract: While fat-tailed densities commonly arise as posterior and marginal distributions in robust models and scale mixtures, they present challenges when Gaussian-based variational inference fails to capture tail decay accurately. We first... |
Title: Fast and realistic large-scale structure from machine-learning-augmented random field simulations Abstract: Producing thousands of simulations of the dark matter distribution in the Universe with increasing precision is a challenging but critical task to facilitate the exploitation of current and forthcoming cos... |
Title: Power and limitations of single-qubit native quantum neural networks Abstract: Quantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and optimization. While the applications of QNN have been widely investigated, its theoretical foundation rema... |
Title: Loss Landscape Engineering via Data Regulation on PINNs Abstract: Physics-Informed Neural Networks have shown unique utility in parameterising the solution of a well-defined partial differential equation using automatic differentiation and residual losses. Though they provide theoretical guarantees of convergenc... |
Title: Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment Abstract: Edge plasma turbulence is critical to the performance of magnetic confinement fusion devices. Towards better understanding edge turbulence in both theory and experiment, a custom-bui... |
Title: Decision Making for Hierarchical Multi-label Classification with Multidimensional Local Precision Rate Abstract: Hierarchical multi-label classification (HMC) has drawn increasing attention in the past few decades. It is applicable when hierarchical relationships among classes are available and need to be incorp... |
Title: Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning Abstract: We use the "map of elections" approach of Szufa et al. (AAMAS 2020) to analyze several well-known vote distributions. For each of them, we give an explicit formula or an efficient algorithm for computing its freque... |
Title: FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization Abstract: We present FactPEGASUS, an abstractive summarization model that addresses the problem of factuality during pre-training and fine-tuning: (1) We augment the sentence selection strategy of PEGASUS's (Zhang et al., 20... |
Title: Federated Anomaly Detection over Distributed Data Streams Abstract: Sharing of telecommunication network data, for example, even at high aggregation levels, is nowadays highly restricted due to privacy legislation and regulations and other important ethical concerns. It leads to scattering data across institutio... |
Title: GraphHD: Efficient graph classification using hyperdimensional computing Abstract: Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural systems such as high-dimensionality, randomness and a ... |
Title: On the Difficulty of Defending Self-Supervised Learning against Model Extraction Abstract: Self-Supervised Learning (SSL) is an increasingly popular ML paradigm that trains models to transform complex inputs into representations without relying on explicit labels. These representations encode similarity structur... |
Title: CurFi: An automated tool to find the best regression analysis model using curve fitting Abstract: Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually o... |
Title: The Primacy Bias in Deep Reinforcement Learning Abstract: This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. Because of training on progressively growing datasets, deep RL agents incur a risk of... |
Title: Gradient-based Counterfactual Explanations using Tractable Probabilistic Models Abstract: Counterfactual examples are an appealing class of post-hoc explanations for machine learning models. Given input $x$ of class $y_1$, its counterfactual is a contrastive example $x^\prime$ of another class $y_0$. Current app... |
Title: JR2net: A Joint Non-Linear Representation and Recovery Network for Compressive Spectral Imaging Abstract: Deep learning models are state-of-the-art in compressive spectral imaging (CSI) recovery. These methods use a deep neural network (DNN) as an image generator to learn non-linear mapping from compressed measu... |
Title: Efficient Algorithms for Planning with Participation Constraints Abstract: We consider the problem of planning with participation constraints introduced in [Zhang et al., 2022]. In this problem, a principal chooses actions in a Markov decision process, resulting in separate utilities for the principal and the ag... |
Title: Scalable algorithms for physics-informed neural and graph networks Abstract: Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some in... |
Title: Data-Driven Interpolation for Super-Scarce X-Ray Computed Tomography Abstract: We address the problem of reconstructing X-Ray tomographic images from scarce measurements by interpolating missing acquisitions using a self-supervised approach. To do so, we train shallow neural networks to combine two neighbouring ... |
Title: On the inability of Gaussian process regression to optimally learn compositional functions Abstract: We rigorously prove that deep Gaussian process priors can outperform Gaussian process priors if the target function has a compositional structure. To this end, we study information-theoretic lower bounds for post... |
Title: Sharp Asymptotics of Self-training with Linear Classifier Abstract: Self-training (ST) is a straightforward and standard approach in semi-supervised learning, successfully applied to many machine learning problems. The performance of ST strongly depends on the supervised learning method used in the refinement st... |
Title: Prioritizing Corners in OoD Detectors via Symbolic String Manipulation Abstract: For safety assurance of deep neural networks (DNNs), out-of-distribution (OoD) monitoring techniques are essential as they filter spurious input that is distant from the training dataset. This paper studies the problem of systematic... |
Title: An Artificial Neural Network Functionalized by Evolution Abstract: The topology of artificial neural networks has a significant effect on their performance. Characterizing efficient topology is a field of promising research in Artificial Intelligence. However, it is not a trivial task and it is mainly experiment... |
Title: Pest presence prediction using interpretable machine learning Abstract: Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton crops that threatens the yield and the quality of lint. The timely knowledge of the presence of the insects in the field is crucial for effective farm interventions... |
Title: Towards Space-to-Ground Data Availability for Agriculture Monitoring Abstract: The recent advances in machine learning and the availability of free and open big Earth data (e.g., Sentinel missions), which cover large areas with high spatial and temporal resolution, have enabled many agriculture monitoring applic... |
Title: Generalizing to New Tasks via One-Shot Compositional Subgoals Abstract: The ability to generalize to previously unseen tasks with little to no supervision is a key challenge in modern machine learning research. It is also a cornerstone of a future "General AI". Any artificially intelligent agent deployed in a re... |
Title: Prediction of stent under-expansion in calcified coronary arteries using machine-learning on intravascular optical coherence tomography Abstract: BACKGROUND Careful evaluation of the risk of stent under-expansions before the intervention will aid treatment planning, including the application of a pre-stent plaqu... |
Title: From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses Abstract: We propose the Bayes-UCBVI algorithm for reinforcement learning in tabular, stage-dependent, episodic Markov decision process: a natural extension of the Bayes-UCB algorithm by Kaufmann et al. (2012) for multi-armed bandits. Our meth... |
Title: Experimental Validation of Spectral-Spatial Power Evolution Design Using Raman Amplifiers Abstract: We experimentally validate a machine learning-enabled Raman amplification framework, capable of jointly shaping the signal power evolution in two domains: frequency and fiber distance. The proposed experiment addr... |
Title: Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml Abstract: In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolu... |
Title: L3-Net Deep Audio Embeddings to Improve COVID-19 Detection from Smartphone Data Abstract: Smartphones and wearable devices, along with Artificial Intelligence, can represent a game-changer in the pandemic control, by implementing low-cost and pervasive solutions to recognize the development of new diseases at th... |
Title: Conditional Born machine for Monte Carlo events generation Abstract: Generative modeling is a promising task for near-term quantum devices, which can use the stochastic nature of quantum measurements as random source. So called Born machines are purely quantum models and promise to generate probability distribut... |
Title: Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection Abstract: The healthcare landscape is moving from the reactive interventions focused on symptoms treatment to a more proactive prevention, from one-size-fits-all to personalized medicine, and from centralized... |
Title: Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder Abstract: Domain generalization aims to improve the generalization capability of machine learning systems to out-of-distribution (OOD) data. Existing domain generalization techniques embark upon stationary and discrete environmen... |
Title: Taming Continuous Posteriors for Latent Variational Dialogue Policies Abstract: Utilizing amortized variational inference for latent-action reinforcement learning (RL) has been shown to be an effective approach in Task-oriented Dialogue (ToD) systems for optimizing dialogue success. Until now, categorical poster... |
Title: Attacking and Defending Deep Reinforcement Learning Policies Abstract: Recent studies have shown that deep reinforcement learning (DRL) policies are vulnerable to adversarial attacks, which raise concerns about applications of DRL to safety-critical systems. In this work, we adopt a principled way and study the ... |
Title: Model Agnostic Local Explanations of Reject Abstract: The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions. Reject options are a common way of ensuring a sufficiently high certainty of predictions made by the system. While being a... |
Title: Rethinking Reinforcement Learning based Logic Synthesis Abstract: Recently, reinforcement learning has been used to address logic synthesis by formulating the operator sequence optimization problem as a Markov decision process. However, through extensive experiments, we find out that the learned policy makes dec... |
Title: Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents Abstract: In this paper we analyze the qualitative differences between evolutionary strategies and reinforcement learning algorithms by focusing on two popular state-of-the-art algorithms: ... |
Title: Fundamental Laws of Binary Classification Abstract: Finding discriminant functions of minimum risk binary classification systems is a novel geometric locus problem -- that requires solving a system of fundamental locus equations of binary classification -- subject to deep-seated statistical laws. We show that a ... |
Title: Chemical transformer compression for accelerating both training and inference of molecular modeling Abstract: Transformer models have been developed in molecular science with excellent performance in applications including quantitative structure-activity relationship (QSAR) and virtual screening (VS). Compared w... |
Title: Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation Abstract: Sequential recommendation (SR) learns users' preferences by capturing the sequential patterns from users' behaviors evolution. As discussed in many works, user-item interactions of SR generally present the intrinsic power-law di... |
Title: An Empirical Investigation of Representation Learning for Imitation Abstract: Imitation learning often needs a large demonstration set in order to handle the full range of situations that an agent might find itself in during deployment. However, collecting expert demonstrations can be expensive. Recent work in v... |
Title: Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine Abstract: CT and MRI are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about the soft tissue. Thus, fusing the information... |
Title: Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies Abstract: Autonomous open-ended learning is a relevant approach in machine learning and robotics, allowing the design of artificial agents able to acquire goals and motor skills without the necessity of user assigned tasks. A crucial i... |
Title: Towards on-sky adaptive optics control using reinforcement learning Abstract: The direct imaging of potentially habitable Exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based extremely large telescopes. To reach this demanding science goal, the instrum... |
Title: SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization Abstract: One noted issue of vector-quantized variational autoencoder (VQ-VAE) is that the learned discrete representation uses only a fraction of the full capacity of the codebook, also known as codebook collapse. We... |
Title: Automated Mobility Context Detection with Inertial Signals Abstract: Remote monitoring of motor functions is a powerful approach for health assessment, especially among the elderly population or among subjects affected by pathologies that negatively impact their walking capabilities. This is further supported by... |
Title: Reachability Constrained Reinforcement Learning Abstract: Constrained reinforcement learning (CRL) has gained significant interest recently, since safety constraints satisfaction is critical for real-world problems. However, existing CRL methods constraining discounted cumulative costs generally lack rigorous de... |
Title: Wasserstein t-SNE Abstract: Scientific datasets often have hierarchical structure: for example, in surveys, individual participants (samples) might be grouped at a higher level (units) such as their geographical region. In these settings, the interest is often in exploring the structure on the unit level rather ... |
Title: A model aggregation approach for high-dimensional large-scale optimization Abstract: Bayesian optimization (BO) has been widely used in machine learning and simulation optimization. With the increase in computational resources and storage capacities in these fields, high-dimensional and large-scale problems are ... |
Title: A scalable deep learning approach for solving high-dimensional dynamic optimal transport Abstract: The dynamic formulation of optimal transport has attracted growing interests in scientific computing and machine learning, and its computation requires to solve a PDE-constrained optimization problem. The classical... |
Title: The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data Abstract: Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in int... |
Title: KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning Abstract: In general, multiple domain cyberspace security assessments can be implemented by reasoning user's permissions. However, while existing methods include some information from the physical and soc... |
Title: Multi-scale Attention Flow for Probabilistic Time Series Forecasting Abstract: The probability prediction of multivariate time series is a notoriously challenging but practical task. On the one hand, the challenge is how to effectively capture the cross-series correlations between interacting time series, to ach... |
Title: Robust Testing in High-Dimensional Sparse Models Abstract: We consider the problem of robustly testing the norm of a high-dimensional sparse signal vector under two different observation models. In the first model, we are given $n$ i.i.d. samples from the distribution $\mathcal{N}\left(\theta,I_d\right)$ (with u... |
Title: Multiscale reconstruction of porous media based on multiple dictionaries learning Abstract: Digital modeling of the microstructure is important for studying the physical and transport properties of porous media. Multiscale modeling for porous media can accurately characterize macro-pores and micro-pores in a lar... |
Title: Learning-Based sensitivity analysis and feedback design for drug delivery of mixed therapy of cancer in the presence of high model uncertainties Abstract: In this paper, a methodology is proposed that enables to analyze the sensitivity of the outcome of a therapy to unavoidable high dispersion of the patient spe... |
Title: Manifold Characteristics That Predict Downstream Task Performance Abstract: Pretraining methods are typically compared by evaluating the accuracy of linear classifiers, transfer learning performance, or visually inspecting the representation manifold's (RM) lower-dimensional projections. We show that the differe... |
Title: Ergodic variational flows Abstract: This work presents a new class of variational family -- ergodic variational flows -- that not only enables tractable i.i.d. sampling and density evaluation, but also comes with MCMC-like convergence guarantees. Ergodic variational flows consist of a mixture of repeated applica... |
Title: Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization Abstract: Spiking neural network (SNN) operating with asynchronous discrete events shows higher energy efficiency. A popular approach to implement deep SNNs is ANN-SNN conversion combining both efficient training in ANNs and... |
Title: $q$-Munchausen Reinforcement Learning Abstract: The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the current stochastic policy. Though significant improvement has been shown with the Boltzma... |
Title: Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths Abstract: Implicit deep learning has recently become popular in the machine learning community since these implicit models can achieve competitive performance with state-of-the-art deep networks while using significantly less mem... |
Title: Diffusion Models for Adversarial Purification Abstract: Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model. These methods do not make assumptions on the form of attack and the classification model, and thus can defend pre-existing classifi... |
Title: Miutsu: NTU's TaskBot for the Alexa Prize Abstract: This paper introduces Miutsu, National Taiwan University's Alexa Prize TaskBot, which is designed to assist users in completing tasks requiring multiple steps and decisions in two different domains -- home improvement and cooking. We overview our system design ... |
Title: A Deep Reinforcement Learning Blind AI in DareFightingICE Abstract: This paper presents a deep reinforcement learning AI that uses sound as the input on the DareFightingICE platform at the DareFightingICE Competition in IEEE CoG 2022. In this work, an AI that only uses sound as the input is called blind AI. Whil... |
Title: Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning Abstract: Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entropy and sparse entropy. ... |
Title: Optimizing the optimizer for data driven deep neural networks and physics informed neural networks Abstract: We investigate the role of the optimizer in determining the quality of the model fit for neural networks with a small to medium number of parameters. We study the performance of Adam, an algorithm for fir... |
Title: Explanation-Guided Fairness Testing through Genetic Algorithm Abstract: The fairness characteristic is a critical attribute of trusted AI systems. A plethora of research has proposed diverse methods for individual fairness testing. However, they are suffering from three major limitations, i.e., low efficiency, l... |
Title: On the Convergence of the Shapley Value in Parametric Bayesian Learning Games Abstract: Measuring contributions is a classical problem in cooperative game theory where the Shapley value is the most well-known solution concept. In this paper, we establish the convergence property of the Shapley value in parametri... |
Title: Exploring the Learning Difficulty of Data Theory and Measure Abstract: As learning difficulty is crucial for machine learning (e.g., difficulty-based weighting learning strategies), previous literature has proposed a number of learning difficulty measures. However, no comprehensive investigation for learning dif... |
Title: Optimal Randomized Approximations for Matrix based Renyi's Entropy Abstract: The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions, thus has been widely adopted in numerous statistical l... |
Title: Trustworthy Graph Neural Networks: Aspects, Methods and Trends Abstract: Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such... |
Title: TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs Abstract: Temporal Neural Networks (TNNs), inspired from the mammalian neocortex, exhibit energy-efficient online sensory processing capabilities. Recent works have proposed a microarchitecture framework for implementing TN... |
Title: Training neural networks using Metropolis Monte Carlo and an adaptive variant Abstract: We examine the zero-temperature Metropolis Monte Carlo algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical grounds and shown empirically by other authors, ... |
Title: What GPT Knows About Who is Who Abstract: Coreference resolution -- which is a crucial task for understanding discourse and language at large -- has yet to witness widespread benefits from large language models (LLMs). Moreover, coreference resolution systems largely rely on supervised labels, which are highly e... |
Title: SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration Abstract: In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent results in several registration ... |
Title: Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning Abstract: Hybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Recent research proposes various techniques that aim to accu... |
Title: Inverse design of nano-photonic wavelength demultiplexer with a deep neural network approach Abstract: In this paper, we propose a pre-trained-combined neural network (PTCN) as a comprehensive solution to the inverse design of an integrated photonic circuit. By utilizing both the initially pre-trained inverse an... |
Title: Learning Representations for New Sound Classes With Continual Self-Supervised Learning Abstract: In this paper, we present a self-supervised learning framework for continually learning representations for new sound classes. The proposed system relies on a continually trained neural encoder that is trained with s... |
Title: Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Abstract: It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate ke... |
Title: The Splendors and Miseries of Heavisidisation Abstract: Machine Learning (ML) is applicable to scientific problems, i.e. to those which have a well defined answer, only if this answer can be brought to a peculiar form ${\cal G}: X\longrightarrow Z$ with ${\cal G}(\vec x)$ expressed as a combination of iterated H... |
Title: Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models Abstract: The fast execution speed and energy efficiency of analog hardware has made them a strong contender for deployment of deep learning model at the edge. However, there are concerns about the presence of analog noise which ca... |
Title: High-Resolution CMB Lensing Reconstruction with Deep Learning Abstract: Next-generation cosmic microwave background (CMB) surveys are expected to provide valuable information about the primordial universe by creating maps of the mass along the line of sight. Traditional tools for creating these lensing convergen... |
Title: What is an equivariant neural network? Abstract: We explain equivariant neural networks, a notion underlying breakthroughs in machine learning from deep convolutional neural networks for computer vision to AlphaFold 2 for protein structure prediction, without assuming knowledge of equivariance or neural networks... |
Title: Novel Multicolumn Kernel Extreme Learning Machine for Food Detection via Optimal Features from CNN Abstract: Automatic food detection is an emerging topic of interest due to its wide array of applications ranging from detecting food images on social media platforms to filtering non-food photos from the users in ... |
Title: Learning Car Speed Using Inertial Sensors Abstract: A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area using as input a stream of measurements from a low-cost six-axis inertial measurement unit (IMU). Three hours of data was collected by driving through the city of Ash... |
Title: Policy Gradient Method For Robust Reinforcement Learning Abstract: This paper develops the first policy gradient method with global optimality guarantee and complexity analysis for robust reinforcement learning under model mismatch. Robust reinforcement learning is to learn a policy robust to model mismatch betw... |
Title: Reductive MDPs: A Perspective Beyond Temporal Horizons Abstract: Solving general Markov decision processes (MDPs) is a computationally hard problem. Solving finite-horizon MDPs, on the other hand, is highly tractable with well known polynomial-time algorithms. What drives this extreme disparity, and do problems ... |
Title: Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent Abstract: In this paper, we study the statistical limits in terms of Sobolev norms of gradient descent for solving inverse problem from randomly sampled noisy observations using a general class of objective funct... |
Title: Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective Abstract: The lottery ticket hypothesis (LTH) has attracted attention because it can explain why over-parameterized models often show high generalization ability. It is known that when we use iterative magnitude pruning (IMP), which is an a... |
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