text stringlengths 0 4.09k |
|---|
Title: All-Photonic Artificial Neural Network Processor Via Non-linear Optics Abstract: Optics and photonics has recently captured interest as a platform to accelerate linear matrix processing, that has been deemed as a bottleneck in traditional digital electronic architectures. In this paper, we propose an all-photoni... |
Title: Bagged Polynomial Regression and Neural Networks Abstract: Series and polynomial regression are able to approximate the same function classes as neural networks. However, these methods are rarely used in practice, although they offer more interpretability than neural networks. In this paper, we show that a poten... |
Title: Learning to Learn Quantum Turbo Detection Abstract: This paper investigates a turbo receiver employing a variational quantum circuit (VQC). The VQC is configured with an ansatz of the quantum approximate optimization algorithm (QAOA). We propose a 'learning to learn' (L2L) framework to optimize the turbo VQC dec... |
Title: A graph representation of molecular ensembles for polymer property prediction Abstract: Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening ca... |
Title: Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks Abstract: We present a generic and trend-aware curriculum learning approach for graph neural networks. It extends existing approaches by incorporating sample-level loss trends to better discriminate easier from harder sa... |
Title: Classification as Direction Recovery: Improved Guarantees via Scale Invariance Abstract: Modern algorithms for binary classification rely on an intermediate regression problem for computational tractability. In this paper, we establish a geometric distinction between classification and regression that allows ris... |
Title: Frank Wolfe Meets Metric Entropy Abstract: The Frank-Wolfe algorithm has seen a resurgence in popularity due to its ability to efficiently solve constrained optimization problems in machine learning and high-dimensional statistics. As such, there is much interest in establishing when the algorithm may possess a ... |
Title: Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift Abstract: In living organisms, homeostasis is the natural regulation of internal states aimed at maintaining conditions compatible with life. Typical artificial systems are not equipped with comparable regulatory features. Here, we introduc... |
Title: QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators Abstract: As the machine learning and systems community strives to achieve higher energy-efficiency through custom DNN accelerators and model compression techniques, there is a need for a design space exploration framework that i... |
Title: Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability Abstract: Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems. Saliency maps are frequently used to ... |
Title: Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting Abstract: Accurate traffic forecasting is essential for smart cities to achieve traffic control, route planning, and flow detection. Although many spatial-temporal methods are currently proposed, these methods are deficient in... |
Title: Hyperparameter Optimization with Neural Network Pruning Abstract: Since the deep learning model is highly dependent on hyperparameters, hyperparameter optimization is essential in developing deep learning model-based applications, even if it takes a long time. As service development using deep learning models ha... |
Title: The Solvability of Interpretability Evaluation Metrics Abstract: Feature attribution methods are popular for explaining neural network predictions, and they are often evaluated on metrics such as comprehensiveness and sufficiency, which are motivated by the principle that more important features -- as judged by ... |
Title: Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning Abstract: Prediction intervals offer an effective tool for quantifying the uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions,... |
Title: Accurate Fairness: Improving Individual Fairness without Trading Accuracy Abstract: Accuracy and fairness are both crucial aspects for trustworthy machine learning. However, in practice, enhancing one aspect may sacrifice the other inevitably. We propose in this paper a new fairness criterion, accurate fairness,... |
Title: It Isn't Sh!tposting, It's My CAT Posting Abstract: In this paper, we describe a novel architecture which can generate hilarious captions for a given input image. The architecture is split into two halves, i.e. image captioning and hilarious text conversion. The architecture starts with a pre-trained CNN model, ... |
Title: CARNet: A Dynamic Autoencoder for Learning Latent Dynamics in Autonomous Driving Tasks Abstract: Autonomous driving has received a lot of attention in the automotive industry and is often seen as the future of transportation. Passenger vehicles equipped with a wide array of sensors (e.g., cameras, front-facing r... |
Title: Customizing ML Predictions for Online Algorithms Abstract: A popular line of recent research incorporates ML advice in the design of online algorithms to improve their performance in typical instances. These papers treat the ML algorithm as a black-box, and redesign online algorithms to take advantage of ML pred... |
Title: No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL Abstract: The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, testing different hyperparameter configurations directly ... |
Title: A Regression Approach to Learning-Augmented Online Algorithms Abstract: The emerging field of learning-augmented online algorithms uses ML techniques to predict future input parameters and thereby improve the performance of online algorithms. Since these parameters are, in general, real-valued functions, a natur... |
Title: TTAPS: Test-Time Adaption by Aligning Prototypes using Self-Supervision Abstract: Nowadays, deep neural networks outperform humans in many tasks. However, if the input distribution drifts away from the one used in training, their performance drops significantly. Recently published research has shown that adaptin... |
Title: Deep-learned orthogonal basis patterns for fast, noise-robust single-pixel imaging Abstract: Single-pixel imaging (SPI) is a novel, unconventional method that goes beyond the notion of traditional cameras but can be computationally expensive and slow for real-time applications. Deep learning has been proposed as... |
Title: Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method Abstract: Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of PDE problems. However,... |
Title: Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling Abstract: Principled Bayesian deep learning (BDL) does not live up to its potential when we only focus on marginal predictive distributions (marginal predictives). Recent works have highlighted th... |
Title: Probability trees and the value of a single intervention Abstract: The most fundamental problem in statistical causality is determining causal relationships from limited data. Probability trees, which combine prior causal structures with Bayesian updates, have been suggested as a possible solution. In this work,... |
Title: On-device modeling of user's social context and familiar places from smartphone-embedded sensor data Abstract: Context modeling and recognition represent complex tasks that allow mobile and ubiquitous computing applications to adapt to the user's situation. Current solutions mainly focus on limited context infor... |
Title: Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems Abstract: Switching dynamical systems are an expressive model class for the analysis of time-series data. As in many fields within the natural and engineering sciences, the systems under study typically evolve continuously in time, it is na... |
Title: Entity Alignment with Reliable Path Reasoning and Relation-Aware Heterogeneous Graph Transformer Abstract: Entity Alignment (EA) has attracted widespread attention in both academia and industry, which aims to seek entities with same meanings from different Knowledge Graphs (KGs). There are substantial multi-step... |
Title: Evaluation of Transfer Learning for Polish with a Text-to-Text Model Abstract: We introduce a new benchmark for assessing the quality of text-to-text models for Polish. The benchmark consists of diverse tasks and datasets: KLEJ benchmark adapted for text-to-text, en-pl translation, summarization, and question an... |
Title: Property Unlearning: A Defense Strategy Against Property Inference Attacks Abstract: During the training of machine learning models, they may store or "learn" more information about the training data than what is actually needed for the prediction or classification task. This is exploited by property inference a... |
Title: Automating In-Network Machine Learning Abstract: Using programmable network devices to aid in-network machine learning has been the focus of significant research. However, most of the research was of a limited scope, providing a proof of concept or describing a closed-source algorithm. To date, no general soluti... |
Title: World Value Functions: Knowledge Representation for Multitask Reinforcement Learning Abstract: An open problem in artificial intelligence is how to learn and represent knowledge that is sufficient for a general agent that needs to solve multiple tasks in a given world. In this work we propose world value functio... |
Title: Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization Abstract: There is a consensus that focusing only on accuracy in searching for optimal machine learning models amplifies biases contained in the data, leading to unfair predictions and decision su... |
Title: Large Neural Networks Learning from Scratch with Very Few Data and without Regularization Abstract: Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our... |
Title: Bridging the gap between QP-based and MPC-based RL Abstract: Reinforcement learning methods typically use Deep Neural Networks to approximate the value functions and policies underlying a Markov Decision Process. Unfortunately, DNN-based RL suffers from a lack of explainability of the resulting policy. In this p... |
Title: The Kernelized Taylor Diagram Abstract: This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely used Taylor diagram, which is used to visualize similarities between populations. However, the... |
Title: Multi-disciplinary fairness considerations in machine learning for clinical trials Abstract: While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A notable concern is the potential to exace... |
Title: GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy Abstract: Synthetic data generation is a fundamental task for many data management and data science applications. Spatial data is of particular interest, and its sensitive nature often leads to privacy concerns. We introduce GeoPointGAN, a... |
Title: FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting Abstract: Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical informat... |
Title: Price Interpretability of Prediction Markets: A Convergence Analysis Abstract: Prediction markets are long known for prediction accuracy. However, there is still a lack of systematic understanding of how prediction markets aggregate information and why they work so well. This work proposes a multivariate utility... |
Title: Generating Explanations from Deep Reinforcement Learning Using Episodic Memory Abstract: Deep Reinforcement Learning (RL) involves the use of Deep Neural Networks (DNNs) to make sequential decisions in order to maximize reward. For many tasks the resulting sequence of actions produced by a Deep RL policy can be ... |
Title: Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders Abstract: Child welfare agencies across the United States are turning to data-driven predictive technologies (commonly called predictive analytics) which use government administrative data to assist w... |
Title: Deep Features for CBIR with Scarce Data using Hebbian Learning Abstract: Features extracted from Deep Neural Networks (DNNs) have proven to be very effective in the context of Content Based Image Retrieval (CBIR). In recent work, biologically inspired \textit{Hebbian} learning algorithms have shown promises for ... |
Title: SoK: The Impact of Unlabelled Data in Cyberthreat Detection Abstract: Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD) in the recent years. A substantial research effort has been invested in the development of specialized algorithms for CTD tasks. From the operational perspe... |
Title: Representation Learning for Content-Sensitive Anomaly Detection in Industrial Networks Abstract: Using a convGRU-based autoencoder, this thesis proposes a framework to learn spatial-temporal aspects of raw network traffic in an unsupervised and protocol-agnostic manner. The learned representations are used to me... |
Title: One-way Explainability Isn't The Message Abstract: Recent engineering developments in specialised computational hardware, data-acquisition and storage technology have seen the emergence of Machine Learning (ML) as a powerful form of data analysis with widespread applicability beyond its historical roots in the d... |
Title: Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness Abstract: While deep neural networks are sensitive to adversarial noise, sparse coding using the Basis Pursuit (BP) method is robust against such attacks, including its multi-layer extensions. We prove that the stability theorem of BP h... |
Title: Meta-Learning Sparse Compression Networks Abstract: Recent work in Deep Learning has re-imagined the representation of data as functions mapping from a coordinate space to an underlying continuous signal. When such functions are approximated by neural networks this introduces a compelling alternative to the more... |
Title: DL4DS -- Deep Learning for empirical DownScaling Abstract: A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. Dynamical downscaling requires running expensive numerical models at high resolution which can be prohibitive due to long model runti... |
Title: One Explanation to Rule them All -- Ensemble Consistent Explanations Abstract: Transparency is a major requirement of modern AI based decision making systems deployed in real world. A popular approach for achieving transparency is by means of explanations. A wide variety of different explanations have been propo... |
Title: Fast Neural Network based Solving of Partial Differential Equations Abstract: We present a novel method for using Neural Networks (NNs) for finding solutions to a class of Partial Differential Equations (PDEs). Our method builds on recent advances in Neural Radiance Field research (NeRFs) and allows for a NN to ... |
Title: Constraining the Attack Space of Machine Learning Models with Distribution Clamping Preprocessing Abstract: Preprocessing and outlier detection techniques have both been applied to neural networks to increase robustness with varying degrees of success. In this paper, we formalize the ideal preprocessor function ... |
Title: A weakly supervised framework for high-resolution crop yield forecasts Abstract: Predictor inputs and label data for crop yield forecasting are not always available at the same spatial resolution. We propose a deep learning framework that uses high resolution inputs and low resolution labels to produce crop yiel... |
Title: Exploring the Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Out-of-Scope Detection Tasks Abstract: This work explores the intrinsic limitations of the popular one-hot encoding method in classification of intents when detection of out-of-scope (OOS) inputs is required. Although recent work h... |
Title: Learning latent representations for operational nitrogen response rate prediction Abstract: Learning latent representations has aided operational decision-making in several disciplines. Its advantages include uncovering hidden interactions in data and automating procedures which were performed manually in the pa... |
Title: Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation Abstract: Continual learning - learning new tasks in sequence while maintaining performance on old tasks - remains particularly challenging for artificial neural networks. Surprisingly, the amount of forgetting does not increase with the... |
Title: Learning Shared Kernel Models: the Shared Kernel EM algorithm Abstract: Expectation maximisation (EM) is an unsupervised learning method for estimating the parameters of a finite mixture distribution. It works by introducing "hidden" or "latent" variables via Baum's auxiliary function $Q$ that allow the joint da... |
Title: POViT: Vision Transformer for Multi-objective Design and Characterization of Nanophotonic Devices Abstract: We solve a fundamental challenge in semiconductor IC design: the fast and accurate characterization of nanoscale photonic devices. Much like the fusion between AI and EDA, many efforts have been made to ap... |
Title: Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are? Abstract: Millimeter-wave (mmWave) communication systems rely on narrow beams for achieving sufficient receive signal power. Adjusting these beams is typically associated with large training overhead, which becomes particula... |
Title: Slowly Changing Adversarial Bandit Algorithms are Provably Efficient for Discounted MDPs Abstract: Reinforcement learning (RL) generalizes bandit problems with additional difficulties on longer planning horzion and unknown transition kernel. We show that, under some mild assumptions, \textbf{any} slowly changing... |
Title: Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data Abstract: Not all real-world data are labeled, and when labels are not available, it is often costly to obtain them. Moreover, as many algorithms suffer from the curse of dimensionality, reducing the features in the data to a smaller ... |
Title: Multilayer Perceptron Based Stress Evolution Analysis under DC Current Stressing for Multi-segment Wires Abstract: Electromigration (EM) is one of the major concerns in the reliability analysis of very large scale integration (VLSI) systems due to the continuous technology scaling. Accurately predicting the time... |
Title: Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels Abstract: A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. This success is largely attributed to the GP's analytical tractabili... |
Title: On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias Abstract: We study the dynamics and implicit bias of gradient flow (GF) on univariate ReLU neural networks with a single hidden layer in a binary classification setting. We show that when the l... |
Title: Predicting failure characteristics of structural materials via deep learning based on nondestructive void topology Abstract: Accurate predictions of the failure progression of structural materials is critical for preventing failure-induced accidents. Despite considerable mechanics modeling-based efforts, accurat... |
Title: Pluralistic Image Completion with Probabilistic Mixture-of-Experts Abstract: Pluralistic image completion focuses on generating both visually realistic and diverse results for image completion. Prior methods enjoy the empirical successes of this task. However, their used constraints for pluralistic image complet... |
Title: Conformalized Online Learning: Online Calibration Without a Holdout Set Abstract: We develop a framework for constructing uncertainty sets with a valid coverage guarantee in an online setting, in which the underlying data distribution can drastically -- and even adversarially -- shift over time. The technique we... |
Title: Single-Shot Optical Neural Network Abstract: As deep neural networks (DNNs) grow to solve increasingly complex problems, they are becoming limited by the latency and power consumption of existing digital processors. 'Weight-stationary' analog optical and electronic hardware has been proposed to reduce the comput... |
Title: Leveraging Global Binary Masks for Structure Segmentation in Medical Images Abstract: Deep learning (DL) models for medical image segmentation are highly influenced by intensity variations of input images and lack generalization due to primarily utilizing pixels' intensity information for inference. Acquiring su... |
Title: Masked Autoencoders As Spatiotemporal Learners Abstract: This paper studies a conceptually simple extension of Masked Autoencoders (MAE) to spatiotemporal representation learning from videos. We randomly mask out spacetime patches in videos and learn an autoencoder to reconstruct them in pixels. Interestingly, w... |
Title: Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research Abstract: We establish a dataset of over $1.6\times10^4$ experimental images of Bose-Einstein condensates containing solitonic excitations to enable machine learning (ML) for many-body physics research. About 33 % of this datase... |
Title: AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications Abstract: Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. In this paper, we investigate a proof-of-concept approach using auto... |
Title: Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks Abstract: Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents. Recent advances in experience replay propose using Mixup (Zhang et... |
Title: Fast matrix multiplication for binary and ternary CNNs on ARM CPU Abstract: Low-bit quantized neural networks are of great interest in practical applications because they significantly reduce the consumption of both memory and computational resources. Binary neural networks are memory and computationally efficie... |
Title: On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning Abstract: While first-order methods are popular for solving optimization problems that arise in large-scale deep learning problems, they come with some acute deficiencies. To diminish such shortcomings, there has been recent interest in apply... |
Title: A2C is a special case of PPO Abstract: Advantage Actor-critic (A2C) and Proximal Policy Optimization (PPO) are popular deep reinforcement learning algorithms used for game AI in recent years. A common understanding is that A2C and PPO are separate algorithms because PPO's clipped objective appears significantly ... |
Title: Relational representation learning with spike trains Abstract: Relational representation learning has lately received an increase in interest due to its flexibility in modeling a variety of systems like interacting particles, materials and industrial projects for, e.g., the design of spacecraft. A prominent meth... |
Title: DDXPlus: A New Dataset For Automatic Medical Diagnosis Abstract: There has been a rapidly growing interest in Automatic Symptom Detection (ASD) and Automatic Diagnosis (AD) systems in the machine learning research literature, aiming to assist doctors in telemedicine services. These systems are designed to intera... |
Title: An Approach to Investigate Public Opinion, Views, and Perspectives Towards Exoskeleton Technology Abstract: Over the last decade, exoskeletons have had an extensive impact on different disciplines and application domains such as assisted living, military, healthcare, firefighting, and industries, on account of t... |
Title: Stochastic uncertainty analysis of gravity gradient tensor components and their combinations Abstract: Full tensor gravity (FTG) devices provide up to five independent components of the gravity gradient tensor. However, we do not yet have a quantitative understanding of which tensor components or combinations of... |
Title: An Invariant Matching Property for Distribution Generalization under Intervened Response Abstract: The task of distribution generalization concerns making reliable prediction of a response in unseen environments. The structural causal models are shown to be useful to model distribution changes through interventi... |
Title: PreQuEL: Quality Estimation of Machine Translation Outputs in Advance Abstract: We present the task of PreQuEL, Pre-(Quality-Estimation) Learning. A PreQuEL system predicts how well a given sentence will be translated, without recourse to the actual translation, thus eschewing unnecessary resource allocation whe... |
Title: LeRaC: Learning Rate Curriculum Abstract: Most curriculum learning methods require an approach to sort the data samples by difficulty, which is often cumbersome to perform. In this work, we propose a novel curriculum learning approach termed Learning Rate Curriculum (LeRaC), which leverages the use of a differen... |
Title: Computing the ensemble spread from deterministic weather predictions using conditional generative adversarial networks Abstract: Ensemble prediction systems are an invaluable tool for weather forecasting. Practically, ensemble predictions are obtained by running several perturbations of the deterministic control... |
Title: AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider Abstract: The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realize... |
Title: High-Order Multilinear Discriminant Analysis via Order-$\textit{n}$ Tensor Eigendecomposition Abstract: Higher-order data with high dimensionality is of immense importance in many areas of machine learning, computer vision, and video analytics. Multidimensional arrays (commonly referred to as tensors) are used f... |
Title: Hybrid Machine Learning Modeling of Engineering Systems -- A Probabilistic Perspective Tested on a Multiphase Flow Modeling Case Study Abstract: To operate process engineering systems in a safe and reliable manner, predictive models are often used in decision making. In many cases, these are mechanistic first pr... |
Title: A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion Detection Abstract: Anomaly-based intrusion detection promises to detect novel or unknown attacks on industrial control systems by modeling expected system behavior and raising corresponding alarms for any deviations.A... |
Title: Torchhd: An Open-Source Python Library to Support Hyperdimensional Computing Research Abstract: Hyperdimensional Computing (HDC) is a neuro-inspired computing framework that exploits high-dimensional random vector spaces. HDC uses extremely parallelizable arithmetic to provide computational solutions that balanc... |
Title: A Classification of $G$-invariant Shallow Neural Networks Abstract: When trying to fit a deep neural network (DNN) to a $G$-invariant target function with respect to a group $G$, it only makes sense to constrain the DNN to be $G$-invariant as well. However, there can be many different ways to do this, thus raisi... |
Title: Scalable Multi-view Clustering with Graph Filtering Abstract: With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years. Most existing multi-view methods operate in raw feature space and heavily depend on the quality of original feature representation. Mo... |
Title: Constraint-Based Causal Structure Learning from Undersampled Graphs Abstract: Graphical structures estimated by causal learning algorithms from time series data can provide highly misleading causal information if the causal timescale of the generating process fails to match the measurement timescale of the data.... |
Title: Neural ODE Control for Trajectory Approximation of Continuity Equation Abstract: We consider the controllability problem for the continuity equation, corresponding to neural ordinary differential equations (ODEs), which describes how a probability measure is pushedforward by the flow. We show that the controlled... |
Title: Riemannian Metric Learning via Optimal Transport Abstract: We introduce an optimal transport-based model for learning a metric tensor from cross-sectional samples of evolving probability measures on a common Riemannian manifold. We neurally parametrize the metric as a spatially-varying matrix field and efficient... |
Title: Transformer-based Program Synthesis for Low-Data Environments Abstract: Recent advancements in large pre-trained transformer models (GPT2/3, T5) have found use in program synthesis to generate programs that satisfy a set of input/output examples. However, these models perform poorly on long-horizon and low-data ... |
Title: MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes Abstract: We propose a mesh-based neural network (MESH2IR) to generate acoustic impulse responses (IRs) for indoor 3D scenes represented using a mesh. The IRs are used to create a high-quality sound experience in interactive applications a... |
Title: IL-flOw: Imitation Learning from Observation using Normalizing Flows Abstract: We present an algorithm for Inverse Reinforcement Learning (IRL) from expert state observations only. Our approach decouples reward modelling from policy learning, unlike state-of-the-art adversarial methods which require updating the... |
Title: A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks Abstract: Networks and temporal point processes serve as fundamental building blocks for modeling complex dynamic relational data in various domains. We propose the latent space Hawkes (LSH) model, a novel generative model for con... |
Title: Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems Abstract: To best assist human designers with different styles, Machine Learning (ML) systems need to be able to adapt to them. However, there has been relatively little prior work on how and when to best adapt an ML system t... |
Title: Causal Inference from Small High-dimensional Datasets Abstract: Many methods have been proposed to estimate treatment effects with observational data. Often, the choice of the method considers the application's characteristics, such as type of treatment and outcome, confounding effect, and the complexity of the ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.