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Title: Developing a Production System for Purpose of Call Detection in Business Phone Conversations Abstract: For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call. An agent cannot provide support on a call if they do not know why a customer is calling. I... |
Title: Perspectives on Incorporating Expert Feedback into Model Updates Abstract: Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficient consideration on how practitioners should translate dom... |
Title: Structural Dropout for Model Width Compression Abstract: Existing ML models are known to be highly over-parametrized, and use significantly more resources than required for a given task. Prior work has explored compressing models offline, such as by distilling knowledge from larger models into much smaller ones.... |
Title: Multimodal Conversational AI: A Survey of Datasets and Approaches Abstract: As humans, we experience the world with all our senses or modalities (sound, sight, touch, smell, and taste). We use these modalities, particularly sight and touch, to convey and interpret specific meanings. Multimodal expressions are ce... |
Title: Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds Abstract: Executing safe and precise flight maneuvers in dynamic high-speed winds is important for the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the relationship between various wind conditions and its effect... |
Title: Formal limitations of sample-wise information-theoretic generalization bounds Abstract: Some of the tightest information-theoretic generalization bounds depend on the average information between the learned hypothesis and a \emph{single} training example. However, these sample-wise bounds were derived only for \... |
Title: Representation learning with function call graph transformations for malware open set recognition Abstract: Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples tha... |
Title: Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust Abstract: Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals. However, in order to ... |
Title: Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits Abstract: Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has been little research investigating how ML prac... |
Title: A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model Abstract: This paper studies the cooperative learning of two generative flow models, in which the two models are iteratively updated based on the jointly synthesized examples. The first flow model is a norma... |
Title: Toward a Geometrical Understanding of Self-supervised Contrastive Learning Abstract: Self-supervised learning (SSL) is currently one of the premier techniques to create data representations that are actionable for transfer learning in the absence of human annotations. Despite their success, the underlying geomet... |
Title: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps Abstract: In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning. Machine learning practitioners often explore image datasets by generating a grid of image... |
Title: Efficient Learning of Interpretable Classification Rules Abstract: Machine learning has become omnipresent with applications in various safety-critical domains such as medical, law, and transportation. In these domains, high-stake decisions provided by machine learning necessitate researchers to design interpret... |
Title: Unified Distributed Environment Abstract: We propose Unified Distributed Environment (UDE), an environment virtualization toolkit for reinforcement learning research. UDE is designed to integrate environments built on any simulation platform such as Gazebo, Unity, Unreal, and OpenAI Gym. Through environment virt... |
Title: Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data Abstract: Physics-informed extreme learning machine (PIELM) has recently received significant attention as a rapid version of physics-informed neural network (PINN) for solving partial differential equations (... |
Title: Improved Consistency Training for Semi-Supervised Sequence-to-Sequence ASR via Speech Chain Reconstruction and Self-Transcribing Abstract: Consistency regularization has recently been applied to semi-supervised sequence-to-sequence (S2S) automatic speech recognition (ASR). This principle encourages an ASR model ... |
Title: No-regret learning for repeated non-cooperative games with lossy bandits Abstract: This paper considers no-regret learning for repeated continuous-kernel games with lossy bandit feedback. Since it is difficult to give the explicit model of the utility functions in dynamic environments, the players' action can on... |
Title: Mask CycleGAN: Unpaired Multi-modal Domain Translation with Interpretable Latent Variable Abstract: We propose Mask CycleGAN, a novel architecture for unpaired image domain translation built based on CycleGAN, with an aim to address two issues: 1) unimodality in image translation and 2) lack of interpretability ... |
Title: RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects Abstract: There have been remarkable successes in computer vision with deep learning. While such breakthroughs show robust performance, there have still been many challenges in learning in-depth knowledge, like occlusion or predicting physical inte... |
Title: QHD: A brain-inspired hyperdimensional reinforcement learning algorithm Abstract: Reinforcement Learning (RL) has opened up new opportunities to solve a wide range of complex decision-making tasks. However, modern RL algorithms, e.g., Deep Q-Learning, are based on deep neural networks, putting high computational... |
Title: RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL Abstract: Relational structures such as schema linking and schema encoding have been validated as a key component to qualitatively translating natural language into SQL queries. However, introducing these structural relations ... |
Title: Verifying Neural Networks Against Backdoor Attacks Abstract: Neural networks have achieved state-of-the-art performance in solving many problems, including many applications in safety/security-critical systems. Researchers also discovered multiple security issues associated with neural networks. One of them is b... |
Title: PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning Abstract: In this work, we present a reinforcement learning (RL) based approach to designing parallel prefix circuits such as adders or priority encoders that are fundamental to high-performance digital design. Unlike prior meth... |
Title: Integration of Text and Graph-based Features for Detecting Mental Health Disorders from Voice Abstract: With the availability of voice-enabled devices such as smart phones, mental health disorders could be detected and treated earlier, particularly post-pandemic. The current methods involve extracting features d... |
Title: SaiNet: Stereo aware inpainting behind objects with generative networks Abstract: In this work, we present an end-to-end network for stereo-consistent image inpainting with the objective of inpainting large missing regions behind objects. The proposed model consists of an edge-guided UNet-like network using Part... |
Title: Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments Abstract: Visualizing optimization landscapes has led to many fundamental insights in numeric optimization, and novel improvements to optimization techniques. However, visualizations of the objective that reinforcement learning optimi... |
Title: High Performance of Gradient Boosting in Binding Affinity Prediction Abstract: Prediction of protein-ligand (PL) binding affinity remains the key to drug discovery. Popular approaches in recent years involve graph neural networks (GNNs), which are used to learn the topology and geometry of PL complexes. However,... |
Title: Fake News Quick Detection on Dynamic Heterogeneous Information Networks Abstract: The spread of fake news has caused great harm to society in recent years. So the quick detection of fake news has become an important task. Some current detection methods often model news articles and other related components as a ... |
Title: Generalization error bounds for DECONET: a deep unfolding network for analysis Compressive Sensing Abstract: In this paper, we propose a new deep unfolding neural network -- based on a state-of-the-art optimization algorithm -- for analysis Compressed Sensing. The proposed network called Decoding Network (DECONE... |
Title: GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters Abstract: Playing games with cheaters is not fun, and in a multi-billion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, consequently, the user experience of their games by ... |
Title: MIND: Maximum Mutual Information Based Neural Decoder Abstract: We are assisting at a growing interest in the development of learning architectures with application to digital communication systems. Herein, we consider the detection/decoding problem. We aim at developing an optimal neural architecture for such a... |
Title: A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation Abstract: In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joi... |
Title: Spiking Approximations of the MaxPooling Operation in Deep SNNs Abstract: Spiking Neural Networks (SNNs) are an emerging domain of biologically inspired neural networks that have shown promise for low-power AI. A number of methods exist for building deep SNNs, with Artificial Neural Network (ANN)-to-SNN conversi... |
Title: Robust Regularized Low-Rank Matrix Models for Regression and Classification Abstract: While matrix variate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient estimation are highly affected by high dimensional ... |
Title: Unsupervised Abnormal Traffic Detection through Topological Flow Analysis Abstract: Cyberthreats are a permanent concern in our modern technological world. In the recent years, sophisticated traffic analysis techniques and anomaly detection (AD) algorithms have been employed to face the more and more subversive ... |
Title: SystemMatch: optimizing preclinical drug models to human clinical outcomes via generative latent-space matching Abstract: Translating the relevance of preclinical models ($\textit{in vitro}$, animal models, or organoids) to their relevance in humans presents an important challenge during drug development. The ri... |
Title: Practical Insights of Repairing Model Problems on Image Classification Abstract: Additional training of a deep learning model can cause negative effects on the results, turning an initially positive sample into a negative one (degradation). Such degradation is possible in real-world use cases due to the diversit... |
Title: Efficient Deep Learning Methods for Identification of Defective Casting Products Abstract: Quality inspection has become crucial in any large-scale manufacturing industry recently. In order to reduce human error, it has become imperative to use efficient and low computational AI algorithms to identify such defec... |
Title: Revisiting Facial Key Point Detection: An Efficient Approach Using Deep Neural Networks Abstract: Facial landmark detection is a widely researched field of deep learning as this has a wide range of applications in many fields. These key points are distinguishing characteristic points on the face, such as the eye... |
Title: Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks Abstract: The SDSS-IV dataset contains information about various astronomical bodies such as Galaxies, Stars, and Quasars captured by observatories. Inspired by our work on deep multimodal learning, which utilized transf... |
Title: Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training Abstract: When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted ... |
Title: BackLink: Supervised Local Training with Backward Links Abstract: Empowered by the backpropagation (BP) algorithm, deep neural networks have dominated the race in solving various cognitive tasks. The restricted training pattern in the standard BP requires end-to-end error propagation, causing large memory cost a... |
Title: Trajectory Inference via Mean-field Langevin in Path Space Abstract: Trajectory inference aims at recovering the dynamics of a population from snapshots of its temporal marginals. To solve this task, a min-entropy estimator relative to the Wiener measure in path space was introduced by Lavenant et al. arXiv:2102... |
Title: Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems Abstract: This paper presents a novel trajectory tracker for autonomous quadrotor navigation in dynamic and complex environments. The proposed framework integrates a distributional Reinforc... |
Title: Evaluating Generalizability of Fine-Tuned Models for Fake News Detection Abstract: The Covid-19 pandemic has caused a dramatic and parallel rise in dangerous misinformation, denoted an `infodemic' by the CDC and WHO. Misinformation tied to the Covid-19 infodemic changes continuously; this can lead to performance... |
Title: Proxyless Neural Architecture Adaptation for Supervised Learning and Self-Supervised Learning Abstract: Recently, Neural Architecture Search (NAS) methods have been introduced and show impressive performance on many benchmarks. Among those NAS studies, Neural Architecture Transformer (NAT) aims to adapt the give... |
Title: Sparsity-Aware Robust Normalized Subband Adaptive Filtering algorithms based on Alternating Optimization Abstract: This paper proposes a unified sparsity-aware robust normalized subband adaptive filtering (SA-RNSAF) algorithm for identification of sparse systems under impulsive noise. The proposed SA-RNSAF algor... |
Title: Fair Bayes-Optimal Classifiers Under Predictive Parity Abstract: Increasing concerns about disparate effects of AI have motivated a great deal of work on fair machine learning. Existing works mainly focus on independence- and separation-based measures (e.g., demographic parity, equality of opportunity, equalized... |
Title: A comparison of PINN approaches for drift-diffusion equations on metric graphs Abstract: In this paper we focus on comparing machine learning approaches for quantum graphs, which are metric graphs, i.e., graphs with dedicated edge lengths, and an associated differential operator. In our case the differential equ... |
Title: Towards a Comprehensive Solution for a Vision-based Digitized Neurological Examination Abstract: The ability to use digitally recorded and quantified neurological exam information is important to help healthcare systems deliver better care, in-person and via telehealth, as they compensate for a growing shortage ... |
Title: Federated learning for LEO constellations via inter-HAP links Abstract: Low Earth Obit (LEO) satellite constellations have seen a sharp increase of deployment in recent years, due to their distinctive capabilities of providing broadband Internet access and enabling global data acquisition as well as large-scale ... |
Title: Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback Abstract: Motivated by applications to online learning in sparse estimation and Bayesian optimization, we consider the problem of online unconstrained nonsubmodular minimization with delayed costs in both full informat... |
Title: Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games Abstract: Imperfect-Information Extensive-Form Games (IIEFGs) is a prevalent model for real-world games involving imperfect information and sequential plays. The Extensive-Form Correlated Equilibrium (EFCE) has been proposed as a natural ... |
Title: RoMFAC: A Robust Mean-Field Actor-Critic Reinforcement Learning against Adversarial Perturbations on States Abstract: Deep reinforcement learning methods for multi-agent systems make optimal decisions dependent on states observed by agents, but a little uncertainty on the observations can possibly mislead agents... |
Title: Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning Abstract: Most machine learning (ML) models are developed for prediction only; offering no option for causal interpretation of their predictions or parameters/properties. This can hamp... |
Title: Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review Abstract: This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes t... |
Title: FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning Abstract: Pseudo labeling and consistency regularization approaches based on confidence thresholding have made great progress in semi-supervised learning (SSL). However, we argue that existing methods might fail to adopt suitable thresholds since... |
Title: Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets Abstract: Deep generative models have achieved tremendous success in designing novel drug molecules in recent years. A new thread of works have shown the great potential in advancing the specificity and success rate of in silico drug design by ... |
Title: Reliable Offline Model-based Optimization for Industrial Process Control Abstract: In the research area of offline model-based optimization, novel and promising methods are frequently developed. However, implementing such methods in real-world industrial systems such as production lines for process control is of... |
Title: Evaluating Independence and Conditional Independence Measures Abstract: Independence and Conditional Independence (CI) are two fundamental concepts in probability and statistics, which can be applied to solve many central problems of statistical inference. There are many existing independence and CI measures def... |
Title: Not to Overfit or Underfit? A Study of Domain Generalization in Question Answering Abstract: Machine learning models are prone to overfitting their source (training) distributions, which is commonly believed to be why they falter in novel target domains. Here we examine the contrasting view that multi-source dom... |
Title: Topic Modelling on Consumer Financial Protection Bureau Data: An Approach Using BERT Based Embeddings Abstract: Customers' reviews and comments are important for businesses to understand users' sentiment about the products and services. However, this data needs to be analyzed to assess the sentiment associated w... |
Title: Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions Abstract: Most graph neural networks (GNNs) rely on the message passing paradigm to propagate node features and build interactions. Recent works point out that different graph learning tasks require different ranges ... |
Title: Textual Explanations and Critiques in Recommendation Systems Abstract: Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual d... |
Title: Supervised Learning and Model Analysis with Compositional Data Abstract: The compositionality and sparsity of high-throughput sequencing data poses a challenge for regression and classification. However, in microbiome research in particular, conditional modeling is an essential tool to investigate relationships ... |
Title: Classifiers are Better Experts for Controllable Text Generation Abstract: This paper proposes a simple method for controllable text generation based on weighting logits produced, namely CAIF sampling. Using an arbitrary third-party text classifier, we adjust a small part of a language model's logits and guide te... |
Title: Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations Abstract: As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting explanations is consistent... |
Title: Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection Abstract: Model attributions are important in deep neural networks as they aid practitioners in understanding the models, but recent studies reveal that attributions can be easily perturbed by adding i... |
Title: A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation Abstract: Several recent studies attempt to address the biological implausibility of the well-known backpropagation (BP) method. While promising methods such as feedback alignment, direct feedback alignment, a... |
Title: Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising Abstract: Predicting user response probabilities is vital for ad ranking and bidding. We hope that predictive models can produce accurate probabilistic predictions that reflect true likelihoods. Calibration te... |
Title: Optimization of Decision Tree Evaluation Using SIMD Instructions Abstract: Decision forest (decision tree ensemble) is one of the most popular machine learning algorithms. To use large models on big data, like document scoring with learning-to-rank models, we need to evaluate these models efficiently. In this pa... |
Title: Finding Global Homophily in Graph Neural Networks When Meeting Heterophily Abstract: We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node's neighborhood with multi-hop neighbors to include more nodes with homophily. However, it is a significant challenge to set pe... |
Title: 3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design Abstract: Deep learning has achieved tremendous success in designing novel chemical compounds with desirable pharmaceutical properties. In this work, we focus on a new type of drug design problem -- generating a small "linker" to p... |
Title: COIN: Communication-Aware In-Memory Acceleration for Graph Convolutional Networks Abstract: Graph convolutional networks (GCNs) have shown remarkable learning capabilities when processing graph-structured data found inherently in many application areas. GCNs distribute the outputs of neural networks embedded in ... |
Title: Generalization Bounds on Multi-Kernel Learning with Mixed Datasets Abstract: This paper presents novel generalization bounds for the multi-kernel learning problem. Motivated by applications in sensor networks, we assume that the dataset is mixed where each sample is taken from a finite pool of Markov chains. Our... |
Title: Parameter Adaptation for Joint Distribution Shifts Abstract: While different methods exist to tackle distinct types of distribution shift, such as label shift (in the form of adversarial attacks) or domain shift, tackling the joint shift setting is still an open problem. Through the study of a joint distribution... |
Title: cMelGAN: An Efficient Conditional Generative Model Based on Mel Spectrograms Abstract: Analysing music in the field of machine learning is a very difficult problem with numerous constraints to consider. The nature of audio data, with its very high dimensionality and widely varying scales of structure, is one of ... |
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... |
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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 ... |
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