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Found 12 papers, 4 papers with code
Tracking Everything Everywhere All at Once
We present a new test-time optimization method for estimating dense and long-range motion from a video sequence.
Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition
We propose Object-Centric Neural Scattering Functions (OSFs) for learning to reconstruct object appearance from only images.
Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows
Understanding and control of Laser-driven Free Electron Lasers remain to be difficult problems that require highly intensive experimental and theoretical research.
ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer
We present ObjectFolder 2. 0, a large-scale, multisensory dataset of common household objects in the form of implicit neural representations that significantly enhances ObjectFolder 1. 0 in three aspects.
Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders
We propose Point2Cyl, a supervised network transforming a raw 3D point cloud to a set of extrusion cylinders.
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ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations
Multisensory object-centric perception, reasoning, and interaction have been a key research topic in recent years.
Data-Driven Shadowgraph Simulation of a 3D Object
In this work we propose a deep neural network based surrogate model for a plasma shadowgraph - a technique for visualization of perturbations in a transparent medium.
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
Temporal networks serve as abstractions of many real-world dynamic systems.
F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams
Edge streams are commonly used to capture interactions in dynamic networks, such as email, social, or computer networks.
A Regulation Enforcement Solution for Multi-agent Reinforcement Learning
If artificially intelligent (AI) agents make decisions on behalf of human beings, we would hope they can also follow established regulations while interacting with humans or other AI agents.
ANS: Adaptive Network Scaling for Deep Rectifier Reinforcement Learning Models
This work provides a thorough study on how reward scaling can affect performance of deep reinforcement learning agents.
A Memory-Network Based Solution for Multivariate Time-Series Forecasting
Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Time-series network (MTNet) for time series forecasting.
