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Title: Action Recognition for American Sign Language Abstract: In this research, we present our findings to recognize American Sign Language from series of hand gestures. While most researches in literature focus only on static handshapes, our work target dynamic hand gestures. Since dynamic signs dataset are very few,... |
Title: Wavelet Feature Maps Compression for Image-to-Image CNNs Abstract: Convolutional Neural Networks (CNNs) are known for requiring extensive computational resources, and quantization is among the best and most common methods for compressing them. While aggressive quantization (i.e., less than 4-bits) performs well ... |
Title: lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents Abstract: Recent advances have shown that SNN-based systems can efficiently perform unsupervised continual learning due to their bio-plausible learning rule, e.g., Spike-Timing-... |
Title: FreDo: Frequency Domain-based Long-Term Time Series Forecasting Abstract: The ability to forecast far into the future is highly beneficial to many applications, including but not limited to climatology, energy consumption, and logistics. However, due to noise or measurement error, it is questionable how far into... |
Title: Fast & Furious: Modelling Malware Detection as Evolving Data Streams Abstract: Malware is a major threat to computer systems and imposes many challenges to cyber security. Targeted threats, such as ransomware, cause millions of dollars in losses every year. The constant increase of malware infections has been mo... |
Title: ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Cases Abstract: Low-quality listings and bad actor behavior in online retail websites threatens e-commerce business as these result in sub-optimal buying experience and erode customer trust. When a new listing is creat... |
Title: Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy Abstract: Among the various aspects of algorithmic fairness studied in recent years, the tension between satisfying both \textit{sufficiency} and \textit{separation} -- e.g. the ratios of positive or negative predictive values, and false positi... |
Title: Certified Robustness Against Natural Language Attacks by Causal Intervention Abstract: Deep learning models have achieved great success in many fields, yet they are vulnerable to adversarial examples. This paper follows a causal perspective to look into the adversarial vulnerability and proposes Causal Intervent... |
Title: K-12BERT: BERT for K-12 education Abstract: Online education platforms are powered by various NLP pipelines, which utilize models like BERT to aid in content curation. Since the inception of the pre-trained language models like BERT, there have also been many efforts toward adapting these pre-trained models to s... |
Title: Women, artificial intelligence, and key positions in collaboration networks: Towards a more equal scientific ecosystem Abstract: Scientific collaboration in almost every discipline is mainly driven by the need of sharing knowledge, expertise, and pooled resources. Science is becoming more complex which has encou... |
Title: Low-rank Optimal Transport: Approximation, Statistics and Debiasing Abstract: The matching principles behind optimal transport (OT) play an increasingly important role in machine learning, a trend which can be observed when OT is used to disambiguate datasets in applications (e.g. single-cell genomics) or used t... |
Title: TorchNTK: A Library for Calculation of Neural Tangent Kernels of PyTorch Models Abstract: We introduce torchNTK, a python library to calculate the empirical neural tangent kernel (NTK) of neural network models in the PyTorch framework. We provide an efficient method to calculate the NTK of multilayer perceptrons... |
Title: Learning to Model Editing Processes Abstract: Most existing sequence generation models produce outputs in one pass, usually left-to-right. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has introduced edit-based model... |
Title: Hardness of Maximum Likelihood Learning of DPPs Abstract: Determinantal Point Processes (DPPs) are a widely used probabilistic model for negatively correlated sets. DPPs have been successfully employed in Machine Learning applications to select a diverse, yet representative subset of data. In seminal work on DPP... |
Title: Imposing Gaussian Pre-Activations in a Neural Network Abstract: The goal of the present work is to propose a way to modify both the initialization distribution of the weights of a neural network and its activation function, such that all pre-activations are Gaussian. We propose a family of pairs initialization/a... |
Title: First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization Abstract: How can we train an assistive human-machine interface (e.g., an electromyography-based limb prosthesis) to translate a user's raw command signals into the actions of a robot or computer when there is no prior ma... |
Title: PLAtE: A Large-scale Dataset for List Page Web Extraction Abstract: Recently, neural models have been leveraged to significantly improve the performance of information extraction from semi-structured websites. However, a barrier for continued progress is the small number of datasets large enough to train these m... |
Title: Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks Abstract: Learning effective recipe representations is essential in food studies. Unlike what has been developed for image-based recipe retrieval or learning structural text embeddings, the combined effect of multi-modal informatio... |
Title: Sparse Mixers: Combining MoE and Mixing to build a more efficient BERT Abstract: We combine the capacity of sparsely gated Mixture-of-Experts (MoE) with the speed and stability of linear, mixing transformations to design the Sparse Mixer encoder model. The Sparse Mixer slightly outperforms (<1%) BERT on GLUE and... |
Title: Reward Uncertainty for Exploration in Preference-based Reinforcement Learning Abstract: Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible reward model based on human preferences by activel... |
Title: Multi-Head Online Learning for Delayed Feedback Modeling Abstract: In online advertising, it is highly important to predict the probability and the value of a conversion (e.g., a purchase). It not only impacts user experience by showing relevant ads, but also affects ROI of advertisers and revenue of marketplace... |
Title: Convolutional Neural Processes for Inpainting Satellite Images Abstract: The widespread availability of satellite images has allowed researchers to model complex systems such as disease dynamics. However, many satellite images have missing values due to measurement defects, which render them unusable without dat... |
Title: AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models Abstract: Fine-tuning large-scale pre-trained language models to downstream tasks require updating hundreds of millions of parameters. This not only increases the serving cost to store a large copy of the model weights for every t... |
Title: Linear Connectivity Reveals Generalization Strategies Abstract: It is widely accepted in the mode connectivity literature that when two neural networks are trained similarly on the same data, they are connected by a path through parameter space over which test set accuracy is maintained. Under some circumstances... |
Title: Differentially Private AUC Computation in Vertical Federated Learning Abstract: Federated learning has gained great attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple parties. As a sub-category, vertical federated learning (vFL) focuses on the scenario where feat... |
Title: Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret Abstract: We propose a new learning framework that captures the tiered structure of many real-world user-interaction applications, where the users can be divided into two groups based on their different tolerance on explorati... |
Title: Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling Abstract: We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy of reduced order models (ROMs) at a modes... |
Title: Deletion and Insertion Tests in Regression Models Abstract: A basic task in explainable AI (XAI) is to identify the most important features behind a prediction made by a black box function $f$. The insertion and deletion tests of \cite{petsiuk2018rise} are used to judge the quality of algorithms that rank pixels... |
Title: VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection Abstract: This paper presents VulBERTa, a deep learning approach to detect security vulnerabilities in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation pipeline on real-world code from open-source C/C++ proje... |
Title: Non-stationary Bandits with Knapsacks Abstract: In this paper, we study the problem of bandits with knapsacks (BwK) in a non-stationary environment. The BwK problem generalizes the multi-arm bandit (MAB) problem to model the resource consumption associated with playing each arm. At each time, the decision maker/... |
Title: Towards Understanding Label Regularization for Fine-tuning Pre-trained Language Models Abstract: Knowledge Distillation (KD) is a prominent neural model compression technique which heavily relies on teacher network predictions to guide the training of a student model. Considering the ever-growing size of pre-tra... |
Title: Additive Logistic Mechanism for Privacy-Preserving Self-Supervised Learning Abstract: We study the privacy risks that are associated with training a neural network's weights with self-supervised learning algorithms. Through empirical evidence, we show that the fine-tuning stage, in which the network weights are ... |
Title: Lyapunov function approach for approximation algorithm design and analysis: with applications in submodular maximization Abstract: We propose a two-phase systematical framework for approximation algorithm design and analysis via Lyapunov function. The first phase consists of using Lyapunov function as an input a... |
Title: Generating Natural Language Proofs with Verifier-Guided Search Abstract: Deductive reasoning (drawing conclusions from assumptions) is a challenging problem in NLP. In this work, we focus on proof generation: given a hypothesis and a set of supporting facts in natural language, the model generates a proof tree i... |
Title: Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation Abstract: Future wireless systems are trending towards higher carrier frequencies that offer larger communication bandwidth but necessitate the use of large antenna arrays. Existing signal processing techniques for channel estimation do not s... |
Title: FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech Abstract: We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark, w... |
Title: Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space Abstract: We study the concentration phenomenon for discrete-time random dynamical systems with an unbounded state space. We develop a heuristic approach towards obtaining exponential concentration inequa... |
Title: MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning Abstract: Many recent breakthroughs in multi-agent reinforcement learning (MARL) require the use of deep neural networks, which are challenging for human experts to interpret and understand. On the other hand, existing ... |
Title: Recipe for a General, Powerful, Scalable Graph Transformer Abstract: We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph ... |
Title: Investigating Information Inconsistency in Multilingual Open-Domain Question Answering Abstract: Retrieval based open-domain QA systems use retrieved documents and answer-span selection over retrieved documents to find best-answer candidates. We hypothesize that multilingual Question Answering (QA) systems are p... |
Title: Linear Algorithms for Nonparametric Multiclass Probability Estimation Abstract: Multiclass probability estimation is the problem of estimating conditional probabilities of a data point belonging to a class given its covariate information. It has broad applications in statistical analysis and data science. Recent... |
Title: Augmentation-induced Consistency Regularization for Classification Abstract: Deep neural networks have become popular in many supervised learning tasks, but they may suffer from overfitting when the training dataset is limited. To mitigate this, many researchers use data augmentation, which is a widely used and ... |
Title: sat2pc: Estimating Point Cloud of Building Roofs from 2D Satellite Images Abstract: Three-dimensional (3D) urban models have gained interest because of their applications in many use-cases such as urban planning and virtual reality. However, generating these 3D representations requires LiDAR data, which are not ... |
Title: FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation Abstract: Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential of functional brain networks construct... |
Title: A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers Abstract: The Variational Quantum Eigensolver (VQE) is a promising candidate for quantum applications on near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Despite a lot of empirical studies and recent progress in theoretical ... |
Title: Federated Self-supervised Learning for Heterogeneous Clients Abstract: Federated Learning has become an important learning paradigm due to its privacy and computational benefits. As the field advances, two key challenges that still remain to be addressed are: (1) system heterogeneity - variability in the compute... |
Title: The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training Abstract: Visual dialog (VisDial) is a task of answering a sequence of questions grounded in an image, using the dialog history as context. Prior work has trained the dialog agents solely on VisDial data via supervised learning or levera... |
Title: Memorization in NLP Fine-tuning Methods Abstract: Large language models are shown to present privacy risks through memorization of training data, and several recent works have studied such risks for the pre-training phase. Little attention, however, has been given to the fine-tuning phase and it is not well unde... |
Title: Exact Phase Transitions in Deep Learning Abstract: This work reports deep-learning-unique first-order and second-order phase transitions, whose phenomenology closely follows that in statistical physics. In particular, we prove that the competition between prediction error and model complexity in the training los... |
Title: Toward Discovering Options that Achieve Faster Planning Abstract: We propose a new objective for option discovery that emphasizes the computational advantage of using options in planning. For a given set of episodic tasks and a given number of options, the objective prefers options that can be used to achieve a ... |
Title: Skill Machines: Temporal Logic Composition in Reinforcement Learning Abstract: A major challenge in reinforcement learning is specifying tasks in a manner that is both interpretable and verifiable. One common approach is to specify tasks through reward machines -- finite state machines that encode the task to be... |
Title: Structured Uncertainty in the Observation Space of Variational Autoencoders Abstract: Variational autoencoders (VAEs) are a popular class of deep generative models with many variants and a wide range of applications. Improvements upon the standard VAE mostly focus on the modelling of the posterior distribution o... |
Title: Is a Question Decomposition Unit All We Need? Abstract: Large Language Models (LMs) have achieved state-of-the-art performance on many Natural Language Processing (NLP) benchmarks. With the growing number of new benchmarks, we build bigger and more complex LMs. However, building new LMs may not be an ideal optio... |
Title: Misleading Deep-Fake Detection with GAN Fingerprints Abstract: Generative adversarial networks (GANs) have made remarkable progress in synthesizing realistic-looking images that effectively outsmart even humans. Although several detection methods can recognize these deep fakes by checking for image artifacts fro... |
Title: RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning Abstract: Prompting has shown impressive success in enabling large pretrained language models (LMs) to perform diverse NLP tasks, especially when only few downstream data are available. Automatically finding the optimal prompt for each task, ... |
Title: Learning from time-dependent streaming data with online stochastic algorithms Abstract: We study stochastic algorithms in a streaming framework, trained on samples coming from a dependent data source. In this streaming framework, we analyze the convergence of Stochastic Gradient (SG) methods in a non-asymptotic ... |
Title: Learning dynamics from partial observations with structured neural ODEs Abstract: Identifying dynamical systems from experimental data is a notably difficult task. Prior knowledge generally helps, but the extent of this knowledge varies with the application, and customized models are often needed. We propose a f... |
Title: Towards a Fair Comparison and Realistic Design and Evaluation Framework of Android Malware Detectors Abstract: As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feat... |
Title: Heterogeneous Reservoir Computing Models for Persian Speech Recognition Abstract: Over the last decade, deep-learning methods have been gradually incorporated into conventional automatic speech recognition (ASR) frameworks to create acoustic, pronunciation, and language models. Although it led to significant imp... |
Title: RobustLR: Evaluating Robustness to Logical Perturbation in Deductive Reasoning Abstract: Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in English natural language. While the progress is promising, it is currently unclear if th... |
Title: ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining Data Abstract: Large pretrained language models have been performing increasingly well in a variety of downstream tasks via prompting. However, it remains unclear from where the model learns the task-spe... |
Title: Learning Distributions by Generative Adversarial Networks: Approximation and Generalization Abstract: We study how well generative adversarial networks (GAN) learn probability distributions from finite samples by analyzing the convergence rates of these models. Our analysis is based on a new oracle inequality th... |
Title: Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes Abstract: Treatments for breast cancer have continued to evolve and improve in recent years, resulting in a substantial increase in survival rates, with approximately 80\% of patients having a 10-year survival period. Given the serious i... |
Title: Autoformalization with Large Language Models Abstract: Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could advance the fields of formal verification, program synthesis, and artificial inte... |
Title: On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity Abstract: Most complex machine learning and modelling techniques are prone to over-fitting and may subsequently generalise poorly to future data. Artificial neural networks are no different in this regard and, despite... |
Title: Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization Abstract: We tackle real-world problems with complex structures beyond the pixel-based game or simulator. We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph tha... |
Title: MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge Abstract: Deep neural network (DNN) latency characterization is a time-consuming process and adds significant cost to Neural Architecture Search (NAS) processes when searching for efficient convolutional neural networks for embedded vision ... |
Title: Training Language Models with Memory Augmentation Abstract: Recent work has improved language models remarkably by equipping them with a non-parametric memory component. However, most existing approaches only introduce memories at testing time, or represent them using a separately trained encoder -- resulting in... |
Title: Rethinking Fano's Inequality in Ensemble Learning Abstract: We propose a fundamental theory on ensemble learning that evaluates a given ensemble system by a well-grounded set of metrics. Previous studies used a variant of Fano's inequality of information theory and derived a lower bound of the classification err... |
Title: Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations Abstract: Despite recent explosion in research interests, in-context learning and the precise impact of the quality of demonstrations remain elusive. While, based on current literature, it is expected that in-context learning shares a simi... |
Title: Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models Abstract: Model compression by way of parameter pruning, quantization, or distillation has recently gained popularity as an approach for reducing the computational requirements of modern deep neural network models for NLP. Pr... |
Title: Surprises in adversarially-trained linear regression Abstract: State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is one of the most effective approaches to defend against such examples. We show that for linear reg... |
Title: Eliciting Transferability in Multi-task Learning with Task-level Mixture-of-Experts Abstract: Recent work suggests that transformer models are capable of multi-task learning on diverse NLP tasks. However, the potential of these models may be limited as they use the same set of parameters for all tasks. In contra... |
Title: Scalable Online Change Detection for High-dimensional Data Streams Abstract: Detecting changes in data streams is a core objective in their analysis and has applications in, say, predictive maintenance, fraud detection, and medicine. A principled approach to detect changes is to compare distributions observed wi... |
Title: VeriFi: Towards Verifiable Federated Unlearning Abstract: Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving par... |
Title: Service Discovery in Social Internet of Things using Graph Neural Networks Abstract: Internet-of-Things (IoT) networks intelligently connect thousands of physical entities to provide various services for the community. It is witnessing an exponential expansion, which is complicating the process of discovering Io... |
Title: DPSNN: A Differentially Private Spiking Neural Network Abstract: Privacy-preserving is a key problem for the machine learning algorithm. Spiking neural network (SNN) plays an important role in many domains, such as image classification, object detection, and speech recognition, but the study on the privacy prote... |
Title: Mathematical Models of Human Drivers Using Artificial Risk Fields Abstract: In this paper, we use the concept of artificial risk fields to predict how human operators control a vehicle in response to upcoming road situations. A risk field assigns a non-negative risk measure to the state of the system in order to... |
Title: Interpretable Feature Engineering for Time Series Predictors using Attention Networks Abstract: Regression problems with time-series predictors are common in banking and many other areas of application. In this paper, we use multi-head attention networks to develop interpretable features and use them to achieve ... |
Title: Deep interpretable ensembles Abstract: Ensembles improve prediction performance and allow uncertainty quantification by aggregating predictions from multiple models. In deep ensembling, the individual models are usually black box neural networks, or recently, partially interpretable semi-structured deep transfor... |
Title: Uncertainty Quantification for Transport in Porous media using Parameterized Physics Informed neural Networks Abstract: We present a Parametrization of the Physics Informed Neural Network (P-PINN) approach to tackle the problem of uncertainty quantification in reservoir engineering problems. We demonstrate the a... |
Title: Machine learning methods for Schlieren imaging of a plasma channel in tenuous atomic vapor Abstract: We investigate the usage of a Schlieren imaging setup to measure the geometrical dimensions of a plasma channel in atomic vapor. Near resonant probe light is used to image the plasma channel in a tenuous vapor an... |
Title: Global geomagnetic perturbation forecasting using Deep Learning Abstract: Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically d... |
Title: Machine learning method for return direction forecasting of Exchange Traded Funds using classification and regression models Abstract: This article aims to propose and apply a machine learning method to analyze the direction of returns from Exchange Traded Funds (ETFs) using the historical return data of its com... |
Title: Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization Abstract: We consider the problem of minimizing the sum of two convex functions. One of those functions has Lipschitz-continuous gradients, and can be accessed via stochastic oracles, whereas the other is "simpl... |
Title: NECA: Network-Embedded Deep Representation Learning for Categorical Data Abstract: We propose NECA, a deep representation learning method for categorical data. Built upon the foundations of network embedding and deep unsupervised representation learning, NECA deeply embeds the intrinsic relationship among attrib... |
Title: An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation Abstract: The performance of machine learning models under distribution shift has been the focus of the community in recent years. Most of current methods have been proposed to improve the robustness to... |
Title: An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems Abstract: Multitask learning assumes that models capable of learning from multiple tasks can achieve better quality and efficiency via knowledge transfer, a key feature of human learning. Though, state of the art ... |
Title: An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation Abstract: Gradient descent or its variants are popular in training neural networks. However, in deep Q-learning with neural network approximation, a type of reinforcement learning, gradient descent (als... |
Title: Residual-Concatenate Neural Network with Deep Regularization Layers for Binary Classification Abstract: Many complex Deep Learning models are used with different variations for various prognostication tasks. The higher learning parameters not necessarily ensure great accuracy. This can be solved by considering c... |
Title: Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors Abstract: Human Activity Recognition (HAR) is a relevant inference task in many mobile applications. State-of-the-art HAR at the edge is typically achieved with lightweight machine learning models such as decision trees and ... |
Title: TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature Abstract: Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural ne... |
Title: Impartial Games: A Challenge for Reinforcement Learning Abstract: The AlphaZero algorithm and its successor MuZero have revolutionised several competitive strategy games, including chess, Go, and shogi and video games like Atari, by learning to play these games better than any human and any specialised computer ... |
Title: Gradient-based explanations for Gaussian Process regression and classification models Abstract: Gaussian Processes (GPs) have proven themselves as a reliable and effective method in probabilistic Machine Learning. Thanks to recent and current advances, modeling complex data with GPs is becoming more and more fea... |
Title: Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently Abstract: Driven by the empirical success and wide use of deep neural networks, understanding the generalization performance of overparameterized models has become an increasingly popular question. To this end, there has been substant... |
Title: Removing the fat from your posterior samples with margarine Abstract: Bayesian workflows often require the introduction of nuisance parameters, yet for core science modelling one needs access to a marginal posterior density. In this work we use masked autoregressive flows and kernel density estimators to encapsu... |
Title: A Universal Error Measure for Input Predictions Applied to Online Graph Problems Abstract: We introduce a novel measure for quantifying the error in input predictions. The error is based on a minimum-cost hyperedge cover in a suitably defined hypergraph and provides a general template which we apply to online gr... |
Title: Stochastic Second-Order Methods Provably Beat SGD For Gradient-Dominated Functions Abstract: We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property which holds in a wide range of applications in machine learning and signal processing.... |
Title: Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images Abstract: We present a novel technique to automatically colorize grayscale images that combine the U-Net model and Fusion Layer features. This approach allows the model to learn the colorization of images from pre-trained U... |
Title: Understanding Programmatic Weak Supervision via Source-aware Influence Function Abstract: Programmatic Weak Supervision (PWS) aggregates the source votes of multiple weak supervision sources into probabilistic training labels, which are in turn used to train an end model. With its increasing popularity, it is cr... |
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