Search Results for author:
Found 12 papers, 2 papers with code
Scaling Laws of Synthetic Data for Language Models
Key findings from our extensive mathematical experiments on SynthLLM include: (1) SynthLLM generates synthetic data that reliably adheres to the rectified scaling law across various model sizes; (2) Performance improvements plateau near 300B tokens; and (3) Larger models approach optimal performance with fewer training tokens.
Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective
Large Language Models (LLMs) have made notable progress in mathematical reasoning, yet often rely on single-paradigm reasoning, limiting their effectiveness across diverse tasks.
Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation
Frontier general-domain models such as GPT-4V still have significant performance gaps in multimodal biomedical applications.
CoDi-2: In-Context Interleaved and Interactive Any-to-Any Generation
We present CoDi-2 a Multimodal Large Language Model (MLLM) for learning in-context interleaved multimodal representations.
CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation
We present CoDi-2, a versatile and interactive Multimodal Large Language Model (MLLM) that can follow complex multimodal interleaved instructions, conduct in-context learning (ICL), reason, chat, edit, etc., in an any-to-any input-output modality paradigm.
i-Code Studio: A Configurable and Composable Framework for Integrative AI
Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities.
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encoder-only models which lack generative abilities.
Multimodal Neural Graph Memory Networks for Visual Question Answering
The MN-GMN uses graph structure with different region features as node attributes and applies a recently proposed powerful graph neural network model, Graph Network (GN), to reason about objects and their interactions in an image.
Learning to Represent Programs with Graphs
Learning tasks on source code (i. e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax.
Relative Facial Action Unit Detection
This paper presents a subject-independent facial action unit (AU) detection method by introducing the concept of relative AU detection, for scenarios where the neutral face is not provided.
Extended Active Learning Method
Active Learning Method (ALM) is a soft computing method which is used for modeling and control, based on fuzzy logic.
Extended Two-Dimensional PCA for Efficient Face Representation and Recognition
r = 1 produces the covariance of 2DPCA, r = n that of PCA.
