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Found 9 papers, 4 papers with code
Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization
Large language models (LLMs) have revolutionized how we interact with technology, but their personalization to individual user preferences remains a significant challenge, particularly in on-device applications.
General targeted machine learning for modern causal mediation analysis
Causal mediation analyses investigate the mechanisms through which causes exert their effects, and are therefore central to scientific progress.
Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials Characterization
Here we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales.
HyperFields: Towards Zero-Shot Generation of NeRFs from Text
We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and (optionally) some fine-tuning.
TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis
The gradual nature of a diffusion process that synthesizes samples in small increments constitutes a key ingredient of Denoising Diffusion Probabilistic Models (DDPM), which have presented unprecedented quality in image synthesis and been recently explored in the motion domain.
DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization
We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization.
A neural net architecture based on principles of neural plasticity and development evolves to effectively catch prey in a simulated environment
A profound challenge for A-Life is to construct agents whose behavior is 'life-like' in a deep way.
Text2Mesh: Text-Driven Neural Stylization for Meshes
In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP.
Ranked #1 on
Neural Stylization
on Meshes
A Scalable and Cloud-Native Hyperparameter Tuning System
In this paper, we introduce Katib: a scalable, cloud-native, and production-ready hyperparameter tuning system that is agnostic of the underlying machine learning framework.
