Search Results for author:
Found 7 papers, 7 papers with code
LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset
Using SMolInstruct, we fine-tune a set of open-source LLMs, among which, we find that Mistral serves as the best base model for chemistry tasks.
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning.
EmoGen: Eliminating Subjective Bias in Emotional Music Generation
In this paper, we propose EmoGen, an emotional music generation system that leverages a set of emotion-related music attributes as the bridge between emotion and music, and divides the generation into two stages: emotion-to-attribute mapping with supervised clustering, and attribute-to-music generation with self-supervised learning.
MuseCoco: Generating Symbolic Music from Text
In contrast, symbolic music offers ease of editing, making it more accessible for users to manipulate specific musical elements.
Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
A recent trend is to use Transformer or its variants in music generation, which is, however, suboptimal, because the full attention cannot efficiently model the typically long music sequences (e. g., over 10, 000 tokens), and the existing models have shortcomings in generating musical repetition structures.
MeloForm: Generating Melody with Musical Form based on Expert Systems and Neural Networks
Specifically, 1) we design an expert system to generate a melody by developing musical elements from motifs to phrases then to sections with repetitions and variations according to pre-given musical form; 2) considering the generated melody is lack of musical richness, we design a Transformer based refinement model to improve the melody without changing its musical form.
Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction
Distantly supervised relation extraction (RE) automatically aligns unstructured text with relation instances in a knowledge base (KB).
