Search Results for author:
Found 13 papers, 5 papers with code
EXAONE Deep: Reasoning Enhanced Language Models
We present EXAONE Deep series, which exhibits superior capabilities in various reasoning tasks, including math and coding benchmarks.
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching
We introduce FragFM, a novel hierarchical framework via fragment-level discrete flow matching for efficient molecular graph generation.
EXAONE 3.5: Series of Large Language Models for Real-world Use Cases
This technical report introduces the EXAONE 3. 5 instruction-tuned language models, developed and released by LG AI Research.
Riemannian Denoising Score Matching for Molecular Structure Optimization with Accurate Energy
Our method has been evaluated by refining several types of starting structures on the QM9 and GEOM datasets, demonstrating that the proposed Riemannian score matching method significantly improves the accuracy of the generated molecular structures, attaining chemical accuracy.
Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
Our approach extends Iterative Markovian Fitting to discrete domains, and we have proved its convergence to the SB.
Efficient Dynamic Hard Negative Sampling for Dialogue Selection
Recent studies have demonstrated significant improvements in selection tasks, and a considerable portion of this success is attributed to incorporating informative negative samples during training.
Ranked #1 on
Conversational Response Selection
on E-commerce
EXAONE 3.0 7.8B Instruction Tuned Language Model
We introduce EXAONE 3. 0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research.
Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers
Understanding transition pathways between two meta-stable states of a molecular system is crucial to advance drug discovery and material design.
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Also, we show that compared to natural language, pseudocode can better guide the reasoning of LMs, even though they are trained to follow natural language instructions.
Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs
The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics.
GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising
Hence, to incorporate information of the correct, GeoTMI aims to maximize mutual information between three variables: the correct and the corrupted geometries and the property.
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions
This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control.
Ranked #1 on
SMAC+
on Off_Superhard_parallel
Ambiguity Adaptive Inference and Single-shot based Channel Pruning for Satellite Processing Environments
In a restricted computing environment like satellite on-board systems, running DL models has limitation on high-speed processing due to the problems such as restriction of available power to consume compared to the relatively high computational complexity.
