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Title: Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL Abstract: Reinforcement learning (RL) agents are commonly evaluated via their expected value over a distribution of test scenarios. Unfortunately, this evaluation approach provides limited evidence for post-deployment generalization beyond the t... |
Title: Interpolating Between Softmax Policy Gradient and Neural Replicator Dynamics with Capped Implicit Exploration Abstract: Neural replicator dynamics (NeuRD) is an alternative to the foundational softmax policy gradient (SPG) algorithm motivated by online learning and evolutionary game theory. The NeuRD expected up... |
Title: A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning Abstract: Adaptive gradient methods have become popular in optimizing deep neural networks; recent examples include AdaGrad and Adam. Although Adam usually converges faster, variations of Adam, for instance, the AdaBelief algorith... |
Title: Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning Abstract: Using deep learning (DL) to accelerate and/or improve scientific workflows can yield discoveries that are otherwise impossible. Unfortunately, DL models have yielded limited success in complex scientific domains due... |
Title: A Neural Network Approach for Homogenization of Multiscale Problems Abstract: We propose a neural network-based approach to the homogenization of multiscale problems. The proposed method uses a derivative-free formulation of a training loss, which incorporates Brownian walkers to find the macroscopic description... |
Title: Guided Deep Metric Learning Abstract: Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning. However, they sometimes lack generalization properties because they are trained often using an inappropriate sample selection strategy or due to the difficulty of the dataset caused ... |
Title: Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning Abstract: The quintessential model-based reinforcement-learning agent iteratively refines its estimates or prior beliefs about the true underlying model of the environment. Recent empirical successes in model-based reinforce... |
Title: Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? Abstract: The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The $L^2$ Physics-Informed Loss is the de-facto standard in training P... |
Title: Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis Abstract: Machine learning approaches commonly rely on the assumption of independent and identically distributed (i.i.d.) data. In reality, however, this assumption is almost always violated due to distribution shifts betw... |
Title: Combinatorial optimization for low bit-width neural networks Abstract: Low-bit width neural networks have been extensively explored for deployment on edge devices to reduce computational resources. Existing approaches have focused on gradient-based optimization in a two-stage train-and-compress setting or as a c... |
Title: CVNets: High Performance Library for Computer Vision Abstract: We introduce CVNets, a high-performance open-source library for training deep neural networks for visual recognition tasks, including classification, detection, and segmentation. CVNets supports image and video understanding tools, including data loa... |
Title: Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning Abstract: Value function estimation is an indispensable subroutine in reinforcement learning, which becomes more challenging in the offline setting. In this paper, we propose Hybrid Value Estimation (HVE) to reduce value estimat... |
Title: MSR: Making Self-supervised learning Robust to Aggressive Augmentations Abstract: Most recent self-supervised learning methods learn visual representation by contrasting different augmented views of images. Compared with supervised learning, more aggressive augmentations have been introduced to further improve t... |
Title: Combinatorial Causal Bandits Abstract: In combinatorial causal bandits (CCB), the learning agent chooses at most $K$ variables in each round to intervene, collects feedback from the observed variables, with the goal of minimizing expected regret on the target variable $Y$. Different from all prior studies on cau... |
Title: Rethinking the Openness of CLIP Abstract: Contrastive Language-Image Pre-training (CLIP) has demonstrated great potential in realizing open-vocabulary image classification in a matching style, because of its holistic use of natural language supervision that covers unconstrained real-world visual concepts. Howeve... |
Title: Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Analysis Abstract: Transport-based metrics and related embeddings (transforms) have recently been used to model signal classes where nonlinear structures or variations are present. In this paper, we study the geodesic properties of time s... |
Title: Modelling and Mining of Patient Pathways: A Scoping Review Abstract: The sequence of visits and procedures performed by the patient in the health system, also known as the patient's pathway or trajectory, can reveal important information about the clinical treatment adopted and the health service provided. The r... |
Title: Future Artificial Intelligence tools and perspectives in medicine Abstract: Purpose of review: Artificial intelligence (AI) has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis. These tools are typically employed on medical data (i.e., image, molecular ... |
Title: Formal Specifications from Natural Language Abstract: We study the ability of language models to translate natural language into formal specifications with complex semantics. In particular, we fine-tune off-the-shelf language models on three datasets consisting of structured English sentences and their correspon... |
Title: C$^3$Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy Abstract: 3D colon reconstruction from Optical Colonoscopy (OC) to detect non-examined surfaces remains an unsolved problem. The challenges arise from the nature of optical colonoscopy data, characterized by highly reflective low-... |
Title: Comparing Performance of Different Linguistically-Backed Word Embeddings for Cyberbullying Detection Abstract: In most cases, word embeddings are learned only from raw tokens or in some cases, lemmas. This includes pre-trained language models like BERT. To investigate on the potential of capturing deeper relatio... |
Title: Exploring the Potential of Feature Density in Estimating Machine Learning Classifier Performance with Application to Cyberbullying Detection Abstract: In this research. we analyze the potential of Feature Density (HD) as a way to comparatively estimate machine learning (ML) classifier performance prior to traini... |
Title: Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile Abstract: Recent years have seen a surge of interest in meta-learning techniques for tackling the few-shot learning (FSL) problem. However, the meta-learner is prone to overfitting since there are only a few available samples, which can b... |
Title: Learning Generative Factors of Neuroimaging Data with Variational auto-encoders Abstract: Neuroimaging techniques produce high-dimensional, stochastic data from which it might be challenging to extract high-level knowledge about the phenomena of interest. We address this challenge by applying the framework of ge... |
Title: Stochastic Multiple Target Sampling Gradient Descent Abstract: Sampling from an unnormalized target distribution is an essential problem with many applications in probabilistic inference. Stein Variational Gradient Descent (SVGD) has been shown to be a powerful method that iteratively updates a set of particles ... |
Title: Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer Abstract: Functional connectivity plays an essential role in modern neuroscience. The modality sheds light on the brain's functional and structural aspects, including mechanisms behind multiple pathologies. One such pathology is schizop... |
Title: Variational Monte Carlo Approach to Partial Differential Equations with Neural Networks Abstract: The accurate numerical solution of partial differential equations is a central task in numerical analysis allowing to model a wide range of natural phenomena by employing specialized solvers depending on the scenari... |
Title: Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity Abstract: Data classification, the process of analyzing data and organizing it into categories, is a fundamental computing problem of natural and artificial information processing systems. Ideally, the performance of classifier models w... |
Title: Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees Abstract: Learning for control of dynamical systems with formal guarantees remains a challenging task. This paper proposes a learning framework to simultaneously stabilize an unknown nonlinear system with a neural controller and learn... |
Title: Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation Abstract: Data augmentation has been proven to be an effective technique for developing machine learning models that are robust to known classes of distributional shifts (e.g., rotations of images), and align... |
Title: Hybrid Architectures for Distributed Machine Learning in Heterogeneous Wireless Networks Abstract: The ever-growing data privacy concerns have transformed machine learning (ML) architectures from centralized to distributed, leading to federated learning (FL) and split learning (SL) as the two most popular privac... |
Title: Soft Adversarial Training Can Retain Natural Accuracy Abstract: Adversarial training for neural networks has been in the limelight in recent years. The advancement in neural network architectures over the last decade has led to significant improvement in their performance. It sparked an interest in their deploym... |
Title: Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios Abstract: Evaluation of intervention in a multi-agent system, e.g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering ... |
Title: Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction Abstract: Evaluating the individual movements for teammates in soccer players is crucial for assessing teamwork, scouting, and fan engagement. It has been said that players in a 90-min game do not have the ball for abo... |
Title: Saliency Attack: Towards Imperceptible Black-box Adversarial Attack Abstract: Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker is only accessible to the model output. Recent studies have devised effective black-box attacks with high query efficiency. H... |
Title: Adaptive Tree Backup Algorithms for Temporal-Difference Reinforcement Learning Abstract: Q($\sigma$) is a recently proposed temporal-difference learning method that interpolates between learning from expected backups and sampled backups. It has been shown that intermediate values for the interpolation parameter ... |
Title: Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection Abstract: In this research, we study the change in the performance of machine learning (ML) classifiers when various linguistic preprocessing methods of a dataset were u... |
Title: Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning Abstract: We expose the danger of reward poisoning in offline multi-agent reinforcement learning (MARL), whereby an attacker can modify the reward vectors to different learners in an offline data set while incurring a poisoning cost. Based on... |
Title: Learning in Congestion Games with Bandit Feedback Abstract: Learning Nash equilibria is a central problem in multi-agent systems. In this paper, we investigate congestion games, a class of games with benign theoretical structure and broad real-world applications. We first propose a centralized algorithm based on... |
Title: An Unpooling Layer for Graph Generation Abstract: We propose a novel and trainable graph unpooling layer for effective graph generation. Given a graph with features, the unpooling layer enlarges this graph and learns its desired new structure and features. Since this unpooling layer is trainable, it can be appli... |
Title: Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball Abstract: In baseball, every play on the field is quantitatively evaluated and has an effect on individual and team strategies. The weighted on base average (wOBA) is well known as a measure of an batter's hittin... |
Title: Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow Abstract: The optimal power flow (OPF) problem, as a critical component of power system operations, becomes increasingly difficult to solve due to the variability, intermittency, and unpredictability of renewable energy brough... |
Title: ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers Abstract: How to efficiently serve ever-larger trained natural language models in practice has become exceptionally challenging even for powerful cloud servers due to their prohibitive memory/computation requirements. In ... |
Title: Extreme Compression for Pre-trained Transformers Made Simple and Efficient Abstract: Extreme compression, particularly ultra-low bit precision (binary/ternary) quantization, has been proposed to fit large NLP models on resource-constraint devices. However, to preserve the accuracy for such aggressive compression... |
Title: Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification Abstract: Realizing human-like perception is a challenge in open driving scenarios due to corner cases and visual occlusions. To gather knowledge of rare and occluded instances, federated learning empowered connected autonomous ... |
Title: Out-of-Distribution Detection using BiGAN and MDL Abstract: We consider the following problem: we have a large dataset of normal data available. We are now given a new, possibly quite small, set of data, and we are to decide if these are normal data, or if they are indicating a new phenomenon. This is a novelty ... |
Title: Coffee Roast Intelligence Abstract: As the coffee industry has grown, there would be more demand for roasted coffee beans, as well as increased rivalry for selling coffee and attracting customers. As the flavor of each variety of coffee is dependent on the degree of roasting of the coffee beans, it is vital to m... |
Title: Differentially Private Model Compression Abstract: Recent papers have shown that large pre-trained language models (LLMs) such as BERT, GPT-2 can be fine-tuned on private data to achieve performance comparable to non-private models for many downstream Natural Language Processing (NLP) tasks while simultaneously ... |
Title: Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization Abstract: This paper considers the generalization performance of differentially private convex learning. We demonstrate that the convergence analysis of Langevin algorithms can be used to obtain new generalization bou... |
Title: Drawing out of Distribution with Neuro-Symbolic Generative Models Abstract: Learning general-purpose representations from perceptual inputs is a hallmark of human intelligence. For example, people can write out numbers or characters, or even draw doodles, by characterizing these tasks as different instantiations... |
Title: Debiased Machine Learning without Sample-Splitting for Stable Estimators Abstract: Estimation and inference on causal parameters is typically reduced to a generalized method of moments problem, which involves auxiliary functions that correspond to solutions to a regression or classification problem. Recent line ... |
Title: A Robust Backpropagation-Free Framework for Images Abstract: While current deep learning algorithms have been successful for a wide variety of artificial intelligence (AI) tasks, including those involving structured image data, they present deep neurophysiological conceptual issues due to their reliance on the g... |
Title: QAGCN: A Graph Convolutional Network-based Multi-Relation Question Answering System Abstract: Answering multi-relation questions over knowledge graphs is a challenging task as it requires multi-step reasoning over a huge number of possible paths. Reasoning-based methods with complex reasoning mechanisms, such as... |
Title: Contrastive learning unifies $t$-SNE and UMAP Abstract: Neighbor embedding methods $t$-SNE and UMAP are the de facto standard for visualizing high-dimensional datasets. They appear to use very different loss functions with different motivations, and the exact relationship between them has been unclear. Here we s... |
Title: Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks Abstract: Deep reinforcement learning has shown promise in discrete domains requiring complex reasoning, including games such as Chess, Go, and Hanabi. However, this type of reasoning is less often observed in long-horizon, continuous domains ... |
Title: Learning Fine Scale Dynamics from Coarse Observations via Inner Recurrence Abstract: Recent work has focused on data-driven learning of the evolution of unknown systems via deep neural networks (DNNs), with the goal of conducting long term prediction of the dynamics of the unknown system. In many real-world appl... |
Title: Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength Abstract: Causal representation learning has been proposed to encode relationships between factors presented in the high dimensional data. However, existing methods suffer from merely using a large amount of labeled data and ign... |
Title: Robust Topological Inference in the Presence of Outliers Abstract: The distance function to a compact set plays a crucial role in the paradigm of topological data analysis. In particular, the sublevel sets of the distance function are used in the computation of persistent homology -- a backbone of the topologica... |
Title: Additive MIL: Intrinsic Interpretability for Pathology Abstract: Multiple Instance Learning (MIL) has been widely applied in pathology towards solving critical problems such as automating cancer diagnosis and grading, predicting patient prognosis, and therapy response. Deploying these models in a clinical settin... |
Title: R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation Abstract: U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. ... |
Title: Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks Abstract: The use of sensors available through smart devices has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on... |
Title: Optimal Competitive-Ratio Control Abstract: Inspired by competitive policy designs approaches in online learning, new control paradigms such as competitive-ratio and regret-optimal control have been recently proposed as alternatives to the classical $\mathcal{H}_2$ and $\mathcal{H}_\infty$ approaches. These comp... |
Title: A Learning-Based Method for Automatic Operator Selection in the Fanoos XAI System Abstract: We describe an extension of the Fanoos XAI system [Bayani et al 2022] which enables the system to learn the appropriate action to take in order to satisfy a user's request for description to be made more or less abstract.... |
Title: Revisiting the "Video" in Video-Language Understanding Abstract: What makes a video task uniquely suited for videos, beyond what can be understood from a single image? Building on recent progress in self-supervised image-language models, we revisit this question in the context of video and language tasks. We pro... |
Title: A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features Abstract: An important characteristic of neural networks is their ability to learn representations of the input data with effective features for prediction, which is believed to be a key factor ... |
Title: Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis Abstract: Randomized smoothing is the dominant standard for provable defenses against adversarial examples. Nevertheless, this method has recently been proven to suffer from important information theoretic limitations. In this paper, we a... |
Title: Compositional Visual Generation with Composable Diffusion Models Abstract: Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions. While such models are highly flexible, they struggle to understand the composition of certain co... |
Title: KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems Abstract: Learning a dynamical system requires stabilizing the unknown dynamics to avoid state blow-ups. However, current reinforcement learning (RL) methods lack stabilization guarantees, which limits th... |
Title: Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank Abstract: Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from biased user click logs. Most of the current ULTR methods are based on the examination hypothesis (EH), which assumes that the click probability can be factor... |
Title: Deep Learning Prediction of Severe Health Risks for Pediatric COVID-19 Patients with a Large Feature Set in 2021 BARDA Data Challenge Abstract: Most children infected with COVID-19 have no or mild symptoms and can recover automatically by themselves, but some pediatric COVID-19 patients need to be hospitalized o... |
Title: Measuring Gender Bias in Word Embeddings of Gendered Languages Requires Disentangling Grammatical Gender Signals Abstract: Does the grammatical gender of a language interfere when measuring the semantic gender information captured by its word embeddings? A number of anomalous gender bias measurements in the embe... |
Title: Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learning Abstract: Gradient based meta-learning methods are prone to overfit on the meta-training set, and this behaviour is more prominent with large and complex networks. Moreover, large networks restrict the application of meta... |
Title: Algorithm for Constrained Markov Decision Process with Linear Convergence Abstract: The problem of constrained Markov decision process is considered. An agent aims to maximize the expected accumulated discounted reward subject to multiple constraints on its costs (the number of constraints is relatively small). ... |
Title: BaCaDI: Bayesian Causal Discovery with Unknown Interventions Abstract: Learning causal structures from observation and experimentation is a central task in many domains. For example, in biology, recent advances allow us to obtain single-cell expression data under multiple interventions such as drugs or gene knoc... |
Title: Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning Abstract: Nowadays, the application of microgrids (MG) with renewable energy is becoming more and more extensive, which creates a strong need for dynamic energy management. In this paper, deep reinforcement learning (DRL... |
Title: Uncertainty Estimation in Machine Learning Abstract: Most machine learning techniques are based upon statistical learning theory, often simplified for the sake of computing speed. This paper is focused on the uncertainty aspect of mathematical modeling in machine learning. Regression analysis is chosen to furthe... |
Title: Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules Abstract: Neural ordinary differential equations (ODEs) have attracted much attention as continuous-time counterparts of deep residual neural networks (NNs), and numerous extensions for recurrent NNs have been pro... |
Title: Multi-user Co-inference with Batch Processing Capable Edge Server Abstract: Graphics processing units (GPUs) can improve deep neural network inference throughput via batch processing, where multiple tasks are concurrently processed. We focus on novel scenarios that the energy-constrained mobile devices offload i... |
Title: PROMISSING: Pruning Missing Values in Neural Networks Abstract: While data are the primary fuel for machine learning models, they often suffer from missing values, especially when collected in real-world scenarios. However, many off-the-shelf machine learning models, including artificial neural network models, a... |
Title: Reinforcement Learning with Neural Radiance Fields Abstract: It is a long-standing problem to find effective representations for training reinforcement learning (RL) agents. This paper demonstrates that learning state representations with supervision from Neural Radiance Fields (NeRFs) can improve the performanc... |
Title: Pruning for Interpretable, Feature-Preserving Circuits in CNNs Abstract: Deep convolutional neural networks are a powerful model class for a range of computer vision problems, but it is difficult to interpret the image filtering process they implement, given their sheer size. In this work, we introduce a method ... |
Title: Beyond Tabula Rasa: Reincarnating Reinforcement Learning Abstract: Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in reinforcement learning (RL) research. However, RL systems, when applied to large-scale settings, rarely operate tabula rasa. Such large-scale systems undergo ... |
Title: Effects of Auxiliary Knowledge on Continual Learning Abstract: In Continual Learning (CL), a neural network is trained on a stream of data whose distribution changes over time. In this context, the main problem is how to learn new information without forgetting old knowledge (i.e., Catastrophic Forgetting). Most... |
Title: MCD: Marginal Contrastive Discrimination for conditional density estimation Abstract: We consider the problem of conditional density estimation, which is a major topic of interest in the fields of statistical and machine learning. Our method, called Marginal Contrastive Discrimination, MCD, reformulates the cond... |
Title: Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games Abstract: We study decentralized policy learning in Markov games where we control a single agent to play with nonstationary and possibly adversarial opponents. Our goal is to develop a no-regret online learning algor... |
Title: Automatic Quantification of Volumes and Biventricular Function in Cardiac Resonance. Validation of a New Artificial Intelligence Approach Abstract: Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ... |
Title: On Calibration of Graph Neural Networks for Node Classification Abstract: Graphs can model real-world, complex systems by representing entities and their interactions in terms of nodes and edges. To better exploit the graph structure, graph neural networks have been developed, which learn entity and edge embeddi... |
Title: Optimal Weak to Strong Learning Abstract: The classic algorithm AdaBoost allows to convert a weak learner, that is an algorithm that produces a hypothesis which is slightly better than chance, into a strong learner, achieving arbitrarily high accuracy when given enough training data. We present a new algorithm t... |
Title: Prescriptive maintenance with causal machine learning Abstract: Machine maintenance is a challenging operational problem, where the goal is to plan sufficient preventive maintenance to avoid machine failures and overhauls. Maintenance is often imperfect in reality and does not make the asset as good as new. Alth... |
Title: Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning Abstract: Characterizing aleatoric and epistemic uncertainty on the predicted rewards can help in building reliable reinforcement learning (RL) systems. Aleatoric uncertainty results from the irreducible environment stochasticity leading... |
Title: Is an encoder within reach? Abstract: The encoder network of an autoencoder is an approximation of the nearest point projection onto the manifold spanned by the decoder. A concern with this approximation is that, while the output of the encoder is always unique, the projection can possibly have infinitely many v... |
Title: Truly Mesh-free Physics-Informed Neural Networks Abstract: Physics-informed Neural Networks (PINNs) have recently emerged as a principled way to include prior physical knowledge in form of partial differential equations (PDEs) into neural networks. Although generally viewed as being mesh-free, current approaches... |
Title: Beyond Opinion Mining: Summarizing Opinions of Customer Reviews Abstract: Customer reviews are vital for making purchasing decisions in the Information Age. Such reviews can be automatically summarized to provide the user with an overview of opinions. In this tutorial, we present various aspects of opinion summa... |
Title: Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination Abstract: Graph contrastive learning (GCL) alleviates the heavy reliance on label information for graph representation learning (GRL) via self-supervised learning schemes. The core idea is to learn by ... |
Title: A High-Performance Customer Churn Prediction System based on Self-Attention Abstract: Customer churn prediction is a challenging domain of research that contributes to customer retention strategy. The predictive performance of existing machine learning models, which are often adopted by churn communities, appear... |
Title: Constraints on parameter choices for successful reservoir computing Abstract: Echo-state networks are simple models of discrete dynamical systems driven by a time series. By selecting network parameters such that the dynamics of the network is contractive, characterized by a negative maximal Lyapunov exponent, t... |
Title: A Survey on Surrogate-assisted Efficient Neural Architecture Search Abstract: Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep... |
Title: Understanding deep learning via decision boundary Abstract: This paper discovers that the neural network with lower decision boundary (DB) variability has better generalizability. Two new notions, algorithm DB variability and $(\epsilon, \eta)$-data DB variability, are proposed to measure the decision boundary v... |
Title: Latent Topology Induction for Understanding Contextualized Representations Abstract: In this work, we study the representation space of contextualized embeddings and gain insight into the hidden topology of large language models. We show there exists a network of latent states that summarize linguistic propertie... |
Title: Canonical convolutional neural networks Abstract: We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical networks as scaled sums of outer vector products. In particular, we train network w... |
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