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Found 14 papers, 5 papers with code
InstantSplat: Unbounded Sparse-view Pose-free Gaussian Splatting in 40 Seconds
This pre-processing is usually conducted via a Structure-from-Motion (SfM) pipeline, a procedure that can be slow and unreliable, particularly in sparse-view scenarios with insufficient matched features for accurate reconstruction.
ChatEd: A Chatbot Leveraging ChatGPT for an Enhanced Learning Experience in Higher Education
With the rapid evolution of Natural Language Processing (NLP), Large Language Models (LLMs) like ChatGPT have emerged as powerful tools capable of transforming various sectors.
Discovery of Small Ultra-short-period Planets Orbiting KG Dwarfs in Kepler Survey Using GPU Phase Folding and Deep Learning Detection System
K01821. b is a sub-Earth with a radius of $0. 648R_\oplus$, orbiting a G dwarf over a 0. 91978-day period.
The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet Transits
This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase Folding and Convolutional Neural Network (CNN) system to detect exoplanets using the transit method.
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS
Recent advancements in real-time neural rendering using point-based techniques have paved the way for the widespread adoption of 3D representations.
Student Mastery or AI Deception? Analyzing ChatGPT's Assessment Proficiency and Evaluating Detection Strategies
Generative AI systems such as ChatGPT have a disruptive effect on learning and assessment.
Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation
For a complicated algorithm, its implementation by a human programmer usually starts with outlining a rough control flow followed by iterative enrichments, eventually yielding carefully generated syntactic structures and variables in a hierarchy.
Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search
Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes.
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
Research in mechanistic interpretability seeks to explain behaviors of machine learning models in terms of their internal components.
Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games
Instead of adding only deterministic best responses to the opponent's least exploitable population mixture, SP-PSRO also learns an approximately optimal stochastic policy and adds it to the population as well.
Anytime PSRO for Two-Player Zero-Sum Games
PSRO is based on the tabular double oracle (DO) method, an algorithm that is guaranteed to converge to a Nash equilibrium, but may increase exploitability from one iteration to the next.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Video-Specific Autoencoders for Exploring, Editing and Transmitting Videos
Associating latent codes of a video and manifold projection enables users to make desired edits.
XDO: A Double Oracle Algorithm for Extensive-Form Games
NXDO is the first deep RL method that can find an approximate Nash equilibrium in high-dimensional continuous-action sequential games.
Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems
The goal of this paper is to present an end-to-end, data-driven framework to control Autonomous Mobility-on-Demand systems (AMoD, i. e. fleets of self-driving vehicles).
Robotics Multiagent Systems Systems and Control Applications
