Search Results for author:
Found 13 papers, 6 papers with code
GaussianTalker: Speaker-specific Talking Head Synthesis via 3D Gaussian Splatting
In this paper, we propose GaussianTalker, a novel method for audio-driven talking head synthesis based on 3D Gaussian Splatting.
IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination
This paper aims to recover object materials from posed images captured under an unknown static lighting condition.
AUTOACT: Automatic Agent Learning from Scratch via Self-Planning
Further analysis demonstrates the effectiveness of the division-of-labor strategy, with the trajectory quality generated by AutoAct significantly outperforming that of others.
FactCHD: Benchmarking Fact-Conflicting Hallucination Detection
Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications.
Making Language Models Better Tool Learners with Execution Feedback
To meet this need, we propose Tool leaRning wIth exeCution fEedback (TRICE), a two-stage end-to-end framework that enables the model to continually learn through feedback derived from tool execution, thereby learning when and how to use tools effectively.
NeuDA: Neural Deformable Anchor for High-Fidelity Implicit Surface Reconstruction
This paper studies implicit surface reconstruction leveraging differentiable ray casting.
Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning
Walle consists of a deployment platform, distributing ML tasks to billion-scale devices in time; a data pipeline, efficiently preparing task input; and a compute container, providing a cross-platform and high-performance execution environment, while facilitating daily task iteration.
Federated Submodel Optimization for Hot and Cold Data Features
We theoretically proved the convergence rate of FedSubAvg by deriving an upper bound under a new metric called the element-wise gradient norm.
Data-Free Evaluation of User Contributions in Federated Learning
We evaluate the performance of PCA and Fed-PCA using the MNIST dataset and a large industrial product recommendation dataset.
Toward Understanding the Influence of Individual Clients in Federated Learning
Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server.
INT8 Winograd Acceleration for Conv1D Equipped ASR Models Deployed on Mobile Devices
The intensive computation of Automatic Speech Recognition (ASR) models obstructs them from being deployed on mobile devices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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MNN: A Universal and Efficient Inference Engine
Deploying deep learning models on mobile devices draws more and more attention recently.
Secure Federated Submodel Learning
Nevertheless, the "position" of a client's truly required submodel corresponds to her private data, and its disclosure to the cloud server during interactions inevitably breaks the tenet of federated learning.
