activity
20222026
most citedKitchenScale: Learning to predict ingredient quantities from recipe contexts

12 citations · 20 across the 12 of their papers we have counts for

collaborators

12 papers

cs.AI2026

ReCurveflow: A Flow Matching Framework that Learns Curved Reaction Trajectories to Predict Transition State Geometries

Seungheun Baek, Mogan Gim, Jaewoo Kang

Predicting transition states (TS) in chemical reactions is crucial, as they provide insights into reaction mechanisms. Recent work on TS prediction have focused on flow matching su…

cs.CY2026

An LLM-based Chain-of-Response Counter-Scam System

Heedou Kim, Mogan Gim, Donghee Choi +4

The rapid evolution of online scams, driven by transnational networks and mass produced social engineering scenarios, has exposed the speed limitations of conventional detection, n…

q-fin.PM2025

DeepAries: Adaptive Rebalancing Interval Selection for Enhanced Portfolio Selection

Jinkyu Kim, Hyunjung Yi, Mogan Gim +2

We propose DeepAries , a novel deep reinforcement learning framework for dynamic portfolio management that jointly optimizes the timing and allocation of rebalancing decisions. Unl…

cs.LG2025

GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization

Seungheun Baek, Soyon Park, Yan Ting Chok +2

Motivation: Predicting cellular responses to genetic perturbations is essential for understanding biological systems and developing targeted therapeutic strategies. While variation…

cs.CL2024

Culinary Class Wars: Evaluating LLMs using ASH in Cuisine Transfer Task

Hoonick Lee, Mogan Gim, Donghyeon Park +2

The advent of Large Language Models (LLMs) have shown promise in various creative domains, including culinary arts. However, many LLMs still struggle to deliver the desired level o…

cs.LG2024★ 2 cited

CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement

Seungheun Baek, Soyon Park, Yan Ting Chok +4

Predicting cellular responses to various perturbations is a critical focus in drug discovery and personalized therapeutics, with deep learning models playing a significant role in…

cs.CE2024★ 1 cited

DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection

Donghee Choi, Jinkyu Kim, Mogan Gim +2

Utilizing market forecasts is pivotal in optimizing portfolio selection strategies. We introduce DeepClair, a novel framework for portfolio selection. DeepClair leverages a transfo…

cs.CL2024

LAPIS: Language Model-Augmented Police Investigation System

Heedou Kim, Dain Kim, Jiwoo Lee +4

Crime situations are race against time. An AI-assisted criminal investigation system, providing prompt but precise legal counsel is in need for police officers. We introduce LAPIS…

cs.AI2024

CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions

Donghee Choi, Mogan Gim, Donghyeon Park +4

This paper introduces CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, a…

cs.LG2024

MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints

Mogan Gim, Jueon Park, Soyon Park +5

Molecular core structures and R-groups are essential concepts in drug development. Integration of these concepts with conventional graph pre-training approaches can promote deeper…

cs.CL2023★ 12 cited

KitchenScale: Learning to predict ingredient quantities from recipe contexts

Donghee Choi, Mogan Gim, Samy Badreddine +3

Determining proper quantities for ingredients is an essential part of cooking practice from the perspective of enriching tastiness and promoting healthiness. We introduce KitchenSc…

cs.IR2022★ 5 cited

RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer

Mogan Gim, Donghee Choi, Kana Maruyama +4

We propose a computational approach for recipe ideation, a downstream task that helps users select and gather ingredients for creating dishes. To perform this task, we developed Re…