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Found 20 papers, 6 papers with code
Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World
Language agents that interact with the world on their own have great potential for automating digital tasks.
ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality
Text presented in augmented reality provides in-situ, real-time information for users.
Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators from High-Resolution Orthographic Imagery and Hybrid Learning
Many areas of the world are without basic information on the socioeconomic well-being of the residing population due to limitations in existing data collection methods.
ESTA: An Esports Trajectory and Action Dataset
Sports, due to their global reach and impact-rich prediction tasks, are an exciting domain to deploy machine learning models.
Graph Neural Networks to Predict Sports Outcomes
To address this issue, we introduce a sport-agnostic graph-based representation of game states.
Calibrate: Interactive Analysis of Probabilistic Model Output
Calibrate constructs a reliability diagram that is resistant to drawbacks in traditional approaches, and allows for interactive subgroup analysis and instance-level inspection.
A Study on Robustness to Perturbations for Representations of Environmental Sound
To accomplish this, we imitate channel effects by injecting perturbations to the audio signal and measure the shift in the new (perturbed) embeddings with three distance measures, making the evaluation domain-dependent but not task-dependent.
Topological Representations of Local Explanations
Furthermore, due to the stochastic nature of some explainability methods, it is possible for different runs of a method to produce contradictory explanations for a given observation.
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials
While designing sustainable and resilient urban built environment is increasingly promoted around the world, significant data gaps have made research on pressing sustainability issues challenging to carry out.
AlphaD3M: Machine Learning Pipeline Synthesis
We introduce AlphaD3M, an automatic machine learning (AutoML) system based on meta reinforcement learning using sequence models with self play.
NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences
Visual place recognition (VPR) is critical in not only localization and mapping for autonomous driving vehicles, but also in assistive navigation for the visually impaired population.
Optimal Team Economic Decisions in Counter-Strike
For example, at the beginning of each round in a Counter-Strike game, teams decide how much of their in-game dollars to spend on equipment.
Valuing Player Actions in Counter-Strike: Global Offensive
Esports, despite its expanding interest, lacks fundamental sports analytics resources such as accessible data or proven and reproducible analytical frameworks.
TopoMap: A 0-dimensional Homology Preserving Projection of High-Dimensional Data
With very few exceptions, projection techniques are designed to map data from a high-dimensional space to a visual space so as to preserve some dissimilarity (similarity) measure, such as the Euclidean distance for example.
Towards Ground Truth Explainability on Tabular Data
In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering.
PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines
In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to search and generate end-to-end learning pipelines.
Human-Computer Interaction
Gradient Dynamics of Shallow Univariate ReLU Networks
We show that the gradient dynamics of such networks are determined by the gradient flow in a non-redundant parameterization of the network function.
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Automatic machine learning is an important problem in the forefront of machine learning.
Deep Geometric Prior for Surface Reconstruction
We propose the use of a deep neural network as a geometric prior for surface reconstruction.
A New Urban Objects Detection Framework Using Weakly Annotated Sets
The large variety and quantity of data available should be explored but this brings important challenges.
