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Found 15 papers, 11 papers with code
Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training
Empirical evaluations across reasoning, planning, and RLHF alignment tasks validate the effectiveness of TS-LLM, even on trees with a depth of 64.
Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields
To this end, we adopt NeRF as the 3D representation and leverage a pre-trained text-to-image diffusion model to constrain the 3D reconstruction of the NeRF to reflect the scene description.
Learning Neural Duplex Radiance Fields for Real-Time View Synthesis
Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual quality.
Order Matters: Agent-by-agent Policy Optimization
In this paper, we propose the \textbf{A}gent-by-\textbf{a}gent \textbf{P}olicy \textbf{O}ptimization (A2PO) algorithm to improve the sample efficiency and retain the guarantees of monotonic improvement for each agent during training.
On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective
The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making (IDM) systems.
Adaptive Joint Optimization for 3D Reconstruction with Differentiable Rendering
Due to inevitable noises introduced during scanning and quantization, 3D reconstruction via RGB-D sensors suffers from errors both in geometry and texture, leading to artifacts such as camera drifting, mesh distortion, texture ghosting, and blurriness.
FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing
Unlike existing dynamic NeRFs that require dense images as input and can only be modeled for a single identity, our method enables face reconstruction across different persons with few-shot inputs.
Bringing Old Films Back to Life
We present a learning-based framework, recurrent transformer network (RTN), to restore heavily degraded old films.
Ranked #6 on
Analog Video Restoration
on TAPE
Neural Auto-Curricula in Two-Player Zero-Sum Games
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.
Multi-agent Reinforcement Learning
Vocal Bursts Valence Prediction
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Our framework is comprised of three key components: (1) a centralized task dispatching model, which supports the self-generated tasks and scalable training with heterogeneous policy combinations; (2) a programming architecture named Actor-Evaluator-Learner, which achieves high parallelism for both training and sampling, and meets the evaluation requirement of auto-curriculum learning; (3) a higher-level abstraction of MARL training paradigms, which enables efficient code reuse and flexible deployments on different distributed computing paradigms.
Neural Auto-Curricula
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.
PD-GAN: Probabilistic Diverse GAN for Image Inpainting
To this end, we propose spatially probabilistic diversity normalization (SPDNorm) inside the modulation to model the probability of generating a pixel conditioned on the context information.
High-Fidelity Pluralistic Image Completion with Transformers
Image completion has made tremendous progress with convolutional neural networks (CNNs), because of their powerful texture modeling capacity.
Ranked #6 on
Image Inpainting
on CelebA-HQ
DeFLOCNet: Deep Image Editing via Flexible Low-level Controls
While existing methods combine an input image and these low-level controls for CNN inputs, the corresponding feature representations are not sufficient to convey user intentions, leading to unfaithfully generated content.
Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud
Thus, Meta-PU even outperforms the existing methods trained for a specific scale factor only.
Graphics
