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Title: The Theoretical Expressiveness of Maxpooling Abstract: Over the decade since deep neural networks became state of the art image classifiers there has been a tendency towards less use of max pooling: the function that takes the largest of nearby pixels in an image. Since max pooling featured prominently in earlie... |
Title: TableFormer: Table Structure Understanding with Transformers Abstract: Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables c... |
Title: Learning in Sparse Rewards settings through Quality-Diversity algorithms Abstract: In the Reinforcement Learning (RL) framework, the learning is guided through a reward signal. This means that in situations of sparse rewards the agent has to focus on exploration, in order to discover which action, or set of acti... |
Title: Discriminating Against Unrealistic Interpolations in Generative Adversarial Networks Abstract: Interpolations in the latent space of deep generative models is one of the standard tools to synthesize semantically meaningful mixtures of generated samples. As the generator function is non-linear, commonly used line... |
Title: SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs Abstract: Entity alignment, aiming to identify equivalent entities across different knowledge graphs (KGs), is a fundamental problem for constructing Web-scale KGs. Over the course of its development, the label supervision has been considered necessary... |
Title: Discontinuous Constituency and BERT: A Case Study of Dutch Abstract: In this paper, we set out to quantify the syntactic capacity of BERT in the evaluation regime of non-context free patterns, as occurring in Dutch. We devise a test suite based on a mildly context-sensitive formalism, from which we derive gramma... |
Title: Reliable validation of Reinforcement Learning Benchmarks Abstract: Reinforcement Learning (RL) is one of the most dynamic research areas in Game AI and AI as a whole, and a wide variety of games are used as its prominent test problems. However, it is subject to the replicability crisis that currently affects mos... |
Title: On-Device Learning: A Neural Network Based Field-Trainable Edge AI Abstract: In real-world edge AI applications, their accuracy is often affected by various environmental factors, such as noises, location/calibration of sensors, and time-related changes. This article introduces a neural network based on-device l... |
Title: Information Gain Propagation: a new way to Graph Active Learning with Soft Labels Abstract: Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort. GNN-based Active Learn... |
Title: Model-agnostic out-of-distribution detection using combined statistical tests Abstract: We present simple methods for out-of-distribution detection using a trained generative model. These techniques, based on classical statistical tests, are model-agnostic in the sense that they can be applied to any differentia... |
Title: Practical Recommendations for the Design of Automatic Fault Detection Algorithms Based on Experiments with Field Monitoring Data Abstract: Automatic fault detection (AFD) is a key technology to optimize the Operation and Maintenance of photovoltaic (PV) systems portfolios. A very common approach to detect faults... |
Title: Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models Abstract: The state-of-the-art Mixture-of-Experts (short as MoE) architecture has achieved several remarkable successes in terms of increasing model capacity. However, MoE has been hindered widespread adoption due to complexity, ... |
Title: The Optimal Noise in Noise-Contrastive Learning Is Not What You Think Abstract: Learning a parametric model of a data distribution is a well-known statistical problem that has seen renewed interest as it is brought to scale in deep learning. Framing the problem as a self-supervised task, where data samples are d... |
Title: Hyperparameter optimization of data-driven AI models on HPC systems Abstract: In the European Center of Excellence in Exascale computing "Research on AI- and Simulation-Based Engineering at Exascale" (CoE RAISE), researchers develop novel, scalable AI technologies towards Exascale. This work exercises High Perfo... |
Title: Pattern Recognition and Event Detection on IoT Data-streams Abstract: Big data streams are possibly one of the most essential underlying notions. However, data streams are often challenging to handle owing to their rapid pace and limited information lifetime. It is difficult to collect and communicate stream sam... |
Title: VaiPhy: a Variational Inference Based Algorithm for Phylogeny Abstract: Phylogenetics is a classical methodology in computational biology that today has become highly relevant for medical investigation of single-cell data, e.g., in the context of development of cancer. The exponential size of the tree space is u... |
Title: A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima Abstract: Bilevel optimization has found extensive applications in modern machine learning problems such as hyperparameter optimization, neural architecture search, meta-learning, etc. While bilevel problems with a unique inner minimal poi... |
Title: Defining a synthetic data generator for realistic electric vehicle charging sessions Abstract: Electric vehicle (EV) charging stations have become prominent in electricity grids in the past years. Analysis of EV charging sessions is useful for flexibility analysis, load balancing, offering incentives to customer... |
Title: Engineering the Neural Automatic Passenger Counter Abstract: Automatic passenger counting (APC) in public transportation has been approached with various machine learning and artificial intelligence methods since its introduction in the 1970s. While equivalence testing is becoming more popular than difference de... |
Title: Rethinking Pretraining as a Bridge from ANNs to SNNs Abstract: Spiking neural networks (SNNs) are known as a typical kind of brain-inspired models with their unique features of rich neuronal dynamics, diverse coding schemes and low power consumption properties. How to obtain a high-accuracy model has always been... |
Title: Discrete Optimal Transport with Independent Marginals is #P-Hard Abstract: We study the computational complexity of the optimal transport problem that evaluates the Wasserstein distance between the distributions of two K-dimensional discrete random vectors. The best known algorithms for this problem run in polyn... |
Title: Speaker recognition improvement using blind inversion of distortions Abstract: In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situati... |
Title: Applying multi-angled parallelism to Spanish topographical maps Abstract: Multi-Angled Parallelism (MAP) is a method to recognize lines in binary images. It is suitable to be implemented in parallel processing and image processing hardware. The binary image is transformed into directional planes, upon which, dir... |
Title: Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics Abstract: We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function. We present a computationally efficient algorithm that attains... |
Title: DCT-Former: Efficient Self-Attention with Discrete Cosine Transform Abstract: Since their introduction the Trasformer architectures emerged as the dominating architectures for both natural language processing and, more recently, computer vision applications. An intrinsic limitation of this family of "fully-atten... |
Title: Hybrid Model-based / Data-driven Graph Transform for Image Coding Abstract: Transform coding to sparsify signal representations remains crucial in an image compression pipeline. While the Karhunen-Lo\`{e}ve transform (KLT) computed from an empirical covariance matrix $\bar{C}$ is theoretically optimal for a stat... |
Title: Model-free Neural Lyapunov Control for Safe Robot Navigation Abstract: Model-free Deep Reinforcement Learning (DRL) controllers have demonstrated promising results on various challenging non-linear control tasks. While a model-free DRL algorithm can solve unknown dynamics and high-dimensional problems, it lacks ... |
Title: Linear Stochastic Bandits over a Bit-Constrained Channel Abstract: One of the primary challenges in large-scale distributed learning stems from stringent communication constraints. While several recent works address this challenge for static optimization problems, sequential decision-making under uncertainty has... |
Title: A Quantitative Geometric Approach to Neural Network Smoothness Abstract: Fast and precise Lipschitz constant estimation of neural networks is an important task for deep learning. Researchers have recently found an intrinsic trade-off between the accuracy and smoothness of neural networks, so training a network w... |
Title: Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data Abstract: Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data. Considering heterogeneo... |
Title: A Simple and Universal Rotation Equivariant Point-cloud Network Abstract: Equivariance to permutations and rigid motions is an important inductive bias for various 3D learning problems. Recently it has been shown that the equivariant Tensor Field Network architecture is universal -- it can approximate any equiva... |
Title: Learning Conditional Variational Autoencoders with Missing Covariates Abstract: Conditional variational autoencoders (CVAEs) are versatile deep generative models that extend the standard VAE framework by conditioning the generative model with auxiliary covariates. The original CVAE model assumes that the data sa... |
Title: Are Latent Factor Regression and Sparse Regression Adequate? Abstract: We propose the Factor Augmented sparse linear Regression Model (FARM) that not only encompasses both the latent factor regression and sparse linear regression as special cases but also bridges dimension reduction and sparse regression togethe... |
Title: Estimating average causal effects from patient trajectories Abstract: In medical practice, treatments are selected based on the expected causal effects on patient outcomes. Here, the gold standard for estimating causal effects are randomized controlled trials; however, such trials are costly and sometimes even u... |
Title: On the application of generative adversarial networks for nonlinear modal analysis Abstract: Linear modal analysis is a useful and effective tool for the design and analysis of structures. However, a comprehensive basis for nonlinear modal analysis remains to be developed. In the current work, a machine learning... |
Title: Convolutional neural networks as an alternative to Bayesian retrievals Abstract: Exoplanet observations are currently analysed with Bayesian retrieval techniques. Due to the computational load of the models used, a compromise is needed between model complexity and computing time. Analysis of data from future fac... |
Title: On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features Abstract: When training deep neural networks for classification tasks, an intriguing empirical phenomenon has been widely observed in the last-layer classifiers and features, where (i) the class means a... |
Title: Flow-based density of states for complex actions Abstract: Emerging sampling algorithms based on normalizing flows have the potential to solve ergodicity problems in lattice calculations. Furthermore, it has been noted that flows can be used to compute thermodynamic quantities which are difficult to access with ... |
Title: Low-Degree Multicalibration Abstract: Introduced as a notion of algorithmic fairness, multicalibration has proved to be a powerful and versatile concept with implications far beyond its original intent. This stringent notion -- that predictions be well-calibrated across a rich class of intersecting subpopulation... |
Title: TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor Abstract: Trajectory generation and prediction are two interwoven tasks that play important roles in planner evaluation and decision making for intelligent vehicles. Most existing methods focus on one of the two and are optimiz... |
Title: Interactive Visualization of Protein RINs using NetworKit in the Cloud Abstract: Network analysis has been applied in diverse application domains. In this paper, we consider an example from protein dynamics, specifically residue interaction networks (RINs). In this context, we use NetworKit -- an established pac... |
Title: Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis Abstract: Virtual high throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often acco... |
Title: Deep Temporal Interpolation of Radar-based Precipitation Abstract: When providing the boundary conditions for hydrological flood models and estimating the associated risk, interpolating precipitation at very high temporal resolutions (e.g. 5 minutes) is essential not to miss the cause of flooding in local region... |
Title: ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural Networks Abstract: To equip Convolutional Neural Networks (CNNs) with explainability, it is essential to interpret how opaque models take specific decisions, understand what causes the errors, improve the architecture design, and... |
Title: Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clustering Abstract: Teachers often conduct surveys in order to collect data from a predefined group of students to gain insights into topics of interest. When analyzing surveys with open-ended textual responses, it is extremely time-consu... |
Title: STEADY: Simultaneous State Estimation and Dynamics Learning from Indirect Observations Abstract: Accurate kinodynamic models play a crucial role in many robotics applications such as off-road navigation and high-speed driving. Many state-of-the-art approaches in learning stochastic kinodynamic models, however, r... |
Title: Evolving Curricula with Regret-Based Environment Design Abstract: It remains a significant challenge to train generally capable agents with reinforcement learning (RL). A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment desig... |
Title: An Analysis of Ensemble Sampling Abstract: Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable. In this paper, we establish a Bayesian regret bound that ensures desirable behavior when ens... |
Title: Supervised Hebbian learning: toward eXplainable AI Abstract: In neural network's Literature, {\em Hebbian learning} traditionally refers to the procedure by which the Hopfield model and its generalizations {\em store} archetypes (i.e., definite patterns that are experienced just once to form the synaptic matrix)... |
Title: HighMMT: Towards Modality and Task Generalization for High-Modality Representation Learning Abstract: Learning multimodal representations involves discovering correspondences and integrating information from multiple heterogeneous sources of data. While recent research has begun to explore the design of more gen... |
Title: Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector Abstract: Cancer is one of the leading causes of death in the developed world. Cancer diagnosis is performed through the microscopic analysis of a sample of suspicious tissue. This process is time consuming and error prone, but Deep Lear... |
Title: Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model Abstract: Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio of stocks with the identification of proper weights of allocation to achieve the opt... |
Title: Hyperspectral Pixel Unmixing with Latent Dirichlet Variational Autoencoder Abstract: Hyperspectral pixel intensities result from a mixing of reflectances from several materials. This paper develops a method of hyperspectral pixel {\it unmixing} that aims to recover the "pure" spectral signal of each material (he... |
Title: Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations Abstract: Deep neural networks have been shown to provide accurate function approximations in high dimensions. However, fitting network parameters requires training data that may not be available beforehand, which is particularl... |
Title: Faking feature importance: A cautionary tale on the use of differentially-private synthetic data Abstract: Synthetic datasets are often presented as a silver-bullet solution to the problem of privacy-preserving data publishing. However, for many applications, synthetic data has been shown to have limited utility... |
Title: Conditional Reconstruction for Open-set Semantic Segmentation Abstract: Open set segmentation is a relatively new and unexploredtask, with just a handful of methods proposed to model suchtasks.We propose a novel method called CoReSeg thattackles the issue using class conditional reconstruction ofthe input images... |
Title: Privacy-Aware Crowd Labelling for Machine Learning Tasks Abstract: The extensive use of online social media has highlighted the importance of privacy in the digital space. As more scientists analyse the data created in these platforms, privacy concerns have extended to data usage within the academia. Although te... |
Title: Stable and Semi-stable Sampling Approaches for Continuously Used Samples Abstract: Information retrieval systems are usually measured by labeling the relevance of results corresponding to a sample of user queries. In practical search engines, such measurement needs to be performed continuously, such as daily or ... |
Title: Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming Abstract: Weak Supervision (WS) techniques allow users to efficiently create large training datasets by programmatically labeling data with heuristic sources of supervision. While the success of WS relies heavily on the provided ... |
Title: A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems Abstract: With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations... |
Title: Skew-Symmetric Adjacency Matrices for Clustering Directed Graphs Abstract: Cut-based directed graph (digraph) clustering often focuses on finding dense within-cluster or sparse between-cluster connections, similar to cut-based undirected graph clustering methods. In contrast, for flow-based clusterings the edges... |
Title: DDL-MVS: Depth Discontinuity Learning for MVS Networks Abstract: Traditional MVS methods have good accuracy but struggle with completeness, while recently developed learning-based multi-view stereo (MVS) techniques have improved completeness except accuracy being compromised. We propose depth discontinuity learn... |
Title: Detecting Chronic Kidney Disease(CKD) at the Initial Stage: A Novel Hybrid Feature-selection Method and Robust Data Preparation Pipeline for Different ML Techniques Abstract: Chronic Kidney Disease (CKD) has infected almost 800 million people around the world. Around 1.7 million people die each year because of i... |
Title: Adaptive Gradient Methods with Local Guarantees Abstract: Adaptive gradient methods are the method of choice for optimization in machine learning and used to train the largest deep models. In this paper we study the problem of learning a local preconditioner, that can change as the data is changing along the opt... |
Title: Estimating Conditional Average Treatment Effects with Missing Treatment Information Abstract: Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE estimation with missing treatments has re... |
Title: Enhancing Adversarial Robustness for Deep Metric Learning Abstract: Owing to security implications of adversarial vulnerability, adversarial robustness of deep metric learning models has to be improved. In order to avoid model collapse due to excessively hard examples, the existing defenses dismiss the min-max a... |
Title: Near-Optimal Correlation Clustering with Privacy Abstract: Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more. In the correlation clustering problem one receives as input a set of nodes and f... |
Title: 3D Common Corruptions and Data Augmentation Abstract: We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural networks. The primary distinction of the proposed transformations is that, unlike exis... |
Title: Continuous-Time Meta-Learning with Forward Mode Differentiation Abstract: Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector fi... |
Title: LILE: Look In-Depth before Looking Elsewhere -- A Dual Attention Network using Transformers for Cross-Modal Information Retrieval in Histopathology Archives Abstract: The volume of available data has grown dramatically in recent years in many applications. Furthermore, the age of networks that used multiple moda... |
Title: Learning Stochastic Parametric Differentiable Predictive Control Policies Abstract: The problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical control-theoretic methods. To address this challenge,... |
Title: Label Leakage and Protection from Forward Embedding in Vertical Federated Learning Abstract: Vertical federated learning (vFL) has gained much attention and been deployed to solve machine learning problems with data privacy concerns in recent years. However, some recent work demonstrated that vFL is vulnerable t... |
Title: Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes Abstract: Bayesian optimization is a technique for optimizing black-box target functions. At the core of Bayesian optimization is a surrogate model that predicts the output of the target function at previously unseen inputs to faci... |
Title: Deep Q-network using reservoir computing with multi-layered readout Abstract: Recurrent neural network (RNN) based reinforcement learning (RL) is used for learning context-dependent tasks and has also attracted attention as a method with remarkable learning performance in recent research. However, RNN-based RL h... |
Title: Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning Abstract: We report the first-time recovery of a fresh meteorite fall using a drone and a machine learning algorithm. A fireball on the 1st April 2021 was observed over Western Australia by the Desert Fireball Network, for which ... |
Title: Weightless Neural Networks for Efficient Edge Inference Abstract: Weightless Neural Networks (WNNs) are a class of machine learning model which use table lookups to perform inference. This is in contrast with Deep Neural Networks (DNNs), which use multiply-accumulate operations. State-of-the-art WNN architecture... |
Title: Modularity of the ABCD Random Graph Model with Community Structure Abstract: The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with similar properties as the we... |
Title: MetaDT: Meta Decision Tree for Interpretable Few-Shot Learning Abstract: Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Recently, lots of methods have been proposed from the perspective of meta-learning and representation learning for improving FSL perform... |
Title: PetsGAN: Rethinking Priors for Single Image Generation Abstract: Single image generation (SIG), described as generating diverse samples that have similar visual content with the given single image, is first introduced by SinGAN which builds a pyramid of GANs to progressively learn the internal patch distribution... |
Title: The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches Abstract: In this paper, we study the problem of regret minimization for episodic Reinforcement Learning (RL) both in the model-free and the model-based setting. We focus on learning with general function classes and gene... |
Title: Large-scale Optimization of Partial AUC in a Range of False Positive Rates Abstract: The area under the ROC curve (AUC) is one of the most widely used performance measures for classification models in machine learning. However, it summarizes the true positive rates (TPRs) over all false positive rates (FPRs) in ... |
Title: Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media Abstract: Physics-Informed Neural Networks (PINNs) have received increased interest for forward, inverse, and surrogate modeling of problems described by partial differential equations (PDE). However, their applic... |
Title: Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations Abstract: Spurious correlations pose a major challenge for robust machine learning. Models trained with empirical risk minimization (ERM) may learn to rely on correlations between class labels and spurious attributes, le... |
Title: An Open Challenge for Inductive Link Prediction on Knowledge Graphs Abstract: An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inf... |
Title: BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning Abstract: Despite the success of deep neural networks, there are still many challenges in deep representation learning due to the data scarcity issues such as data imbalance, unseen distribution, and domain shift. To address... |
Title: Semi-supervised Learning using Robust Loss Abstract: The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural networks. However, the quality of the automatically generated labels can be uneven an... |
Title: Private High-Dimensional Hypothesis Testing Abstract: We provide improved differentially private algorithms for identity testing of high-dimensional distributions. Specifically, for $d$-dimensional Gaussian distributions with known covariance $\Sigma$, we can test whether the distribution comes from $\mathcal{N}... |
Title: Self-supervised Transparent Liquid Segmentation for Robotic Pouring Abstract: Liquid state estimation is important for robotics tasks such as pouring; however, estimating the state of transparent liquids is a challenging problem. We propose a novel segmentation pipeline that can segment transparent liquids such ... |
Title: QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition Abstract: Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency. However, previous prompt-based m... |
Title: Rethinking the role of normalization and residual blocks for spiking neural networks Abstract: Biologically inspired spiking neural networks (SNNs) are widely used to realize ultralow-power energy consumption. However, deep SNNs are not easy to train due to the excessive firing of spiking neurons in the hidden l... |
Title: Automated clustering of COVID-19 anti-vaccine discourse on Twitter Abstract: Attitudes about vaccination have become more polarized; it is common to see vaccine disinformation and fringe conspiracy theories online. An observational study of Twitter vaccine discourse is found in Ojea Quintana et al. (2021): the a... |
Title: A Characterization of Multiclass Learnability Abstract: A seminal result in learning theory characterizes the PAC learnability of binary classes through the Vapnik-Chervonenkis dimension. Extending this characterization to the general multiclass setting has been open since the pioneering works on multiclass PAC ... |
Title: Graph Representation Learning Beyond Node and Homophily Abstract: Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are designed mainly under the node homophily as... |
Title: Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings Abstract: A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is focused on capturing the word co-occurrences in ... |
Title: Data Augmentation as Feature Manipulation: a story of desert cows and grass cows Abstract: Data augmentation is a cornerstone of the machine learning pipeline, yet its theoretical underpinnings remain unclear. Is it merely a way to artificially augment the data set size? Or is it about encouraging the model to s... |
Title: A shallow physics-informed neural network for solving partial differential equations on surfaces Abstract: In this paper, we introduce a mesh-free physics-informed neural network for solving partial differential equations on surfaces. Based on the idea of embedding techniques, we write the underlying surface dif... |
Title: Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models Abstract: Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially for those societal applications, e.g., hiring systems should recommend applicants equally from different demographi... |
Title: Neural Graph Matching for Pre-training Graph Neural Networks Abstract: Recently, graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. However, when adapted to downstream tasks, it usually requires abundant task-specific labeled data, which can be extremely scarce in practic... |
Title: Uniform Approximations for Randomized Hadamard Transforms with Applications Abstract: Randomized Hadamard Transforms (RHTs) have emerged as a computationally efficient alternative to the use of dense unstructured random matrices across a range of domains in computer science and machine learning. For several appl... |
Title: AdaFamily: A family of Adam-like adaptive gradient methods Abstract: We propose AdaFamily, a novel method for training deep neural networks. It is a family of adaptive gradient methods and can be interpreted as sort of a blend of the optimization algorithms Adam, AdaBelief and AdaMomentum. We perform experiments... |
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