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X-WR-CALDESC:Events for Biomedical Mathematics Group
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Seoul:20251212T110000
DTEND;TZID=Asia/Seoul:20251212T123000
DTSTAMP:20260422T160644
CREATED:20251026T141250Z
LAST-MODIFIED:20251215T043112Z
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SUMMARY:Quantifying interventional causality by knockoff operation - Yun Min Song
DESCRIPTION:In this talk\, we discuss the paper “Causal disentanglement for single-cell representations and controllable counterfactual generation” by Yicheng Gao et al.\, Nature Communications\, 2025. \nAbstract  \nConducting disentanglement learning on single-cell omics data offers a promising alternative to traditional black-box representation learning by separating the semantic concepts embedded in a biological process. We present CausCell\, which incorporates the factual information about causal relationships among disentangled concepts within a diffusion model to generate more reliable disentangled cellular representations\, with the aim of increasing the explainability\, generalizability and controllability of single-cell data\, including spatial-temporal omics data\, relative to those of the existing black-box representation learning models. Two quantitative evaluation scenarios\, i.e.\, disentanglement and reconstruction\, are presented to conduct the first comprehensive single-cell disentanglement learning benchmark\, which demonstrates that CausCell outperforms the state-of-the-art methods in both scenarios. Additionally\, CausCell can implement controllable generation by intervening with the concepts of single-cell data when given a causal structure. It also has the potential to uncover biological insights by generating counterfactuals from small and noisy single-cell datasets.
URL:https://www.ibs.re.kr/bimag/event/journal-club-yun-min-song/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251226T100000
DTEND;TZID=Asia/Seoul:20251226T120000
DTSTAMP:20260422T160644
CREATED:20251026T141349Z
LAST-MODIFIED:20251226T002150Z
UID:11798-1766743200-1766750400@www.ibs.re.kr
SUMMARY:N-BEATS: Neural basis expansion analysis for interpretable time series forecasting - Jinwoo Hyun
DESCRIPTION:In this talk\, we discuss the paper “N-BEATS: Neural basis expansion analysis for interpretable time series forecasting” by B. Oreshkin et al.\, ICLR\, 2020. \nAbstract \nWe focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties\, being interpretable\, applicable without modification to a wide array of target domains\, and fast to train. We test the proposed architecture on several well-known datasets\, including M3\, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets\, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year’s winner of the M4 competition\, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that\, contrarily to received wisdom\, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally\, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.
URL:https://www.ibs.re.kr/bimag/event/journal-club-jinwoo-hyun/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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