202502
Deepseek-R1

박세현

Deepseek-R1 PPT

202502
Conformal Prediction

박석훈

Stable Conformal Prediction sets – 2022 ICML

이경선

Derandomized novelty detection with FDR control via conformal e-values – 2023 NeurIPS

박진원

Conformalized fairness via quantile regression – 2022 NeurIPS

신윤섭

Improving Expert Predictions with Conformal Prediction – ICML 2023

이지후

Improving Uncertainty Quanitifcaion of Deep Classifiers via Neighborhood Conformal Prediction – AAAI 2023

안영민

Class-conditional conformal prediction with many classes – 2023 NeurIPS

김건웅

Conformal Prediction with Large Language Models for Multi-choice Question Answering – ICML 2023 workshop

정휘창

Conformal Prediction with Missing Values – 2023 ICML

정한교

Conformal Prediciton via Regression-as-classification – 2024 ICLR

박찬무

Testing for outliers with conformal p-values – 2023 Annals

박세현

Verifiably Robust Conformal Prediction – 2024 NeurIPS

최성식

Conformal meta-learners for predictive inference of individual treatment effects. – 2024 NeurIPS

정유립

Conformal Prediction for Deep Classifier via Label Ranking – 2024 ICML

이해영

Conformal Language Modeling – 2024 ICLR

김초은

An Information Theoretic Perspective on Conformal Prediction – 2024 NeurIPS

이성은

Large language model validity via enhanced conformal prediction methods – 2024 NeurIPS

202501
Statistical learning theory

세미나 자료

ML-Section1

ML-Section2

ML-Section3

ML-Section4

ML-Section5 

Localization_Analysis.pdf

세미나 발표 영상

20241004
LLM with Prof.

이성은

RNN & LSTM

정한교

Seq2Seq & Attention

신윤섭

Transformer

이해영

Speech & Vision Transformer

김초은

GPT1,2,3 + LLM(In-context learning)

박세현

Transformer-based Multivariat time Series. Foundation model for time series

조영래

BERT & GPT

이경선

Multimodal

20240829
LLM

이성은

GPT-2: Language Models are Unsupervised Multitask Learners

정한교

GPT-3: GLanguage models are few show Learners

김초은

LLMs are Zero Shot Reasoners

이경선

CLIP: Learning Transferable Visual Models From Natural Language Supervision

신윤섭

Stable Diffusion: High-Resolution Image Synthesis with Latent Diffusion Models

정휘창

Diffusion Model

20240822
LLM

박세현

Improving Language Understanding by Generative Pre-Training

조영래

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

20240813
LLM

신윤섭

(ViT) AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE (ICLR 2021)

이해영

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

20240808
LLM

정한교

Neural Machine Translation by jointly learning to align and translate

박세현

Attention is all you need

20240806
LLM

김초은

Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network

이해영

• Sequence to Sequence Learning with Neural Networks

20240801
LLM

이경선

• The Evolution of Translation Models: From DNN to Transformer

이성은

• A Critical Review of Recurrent Neural Networks for Sequence Learning

조영래

• Long Short-Term Memory
  

20240321
Fairness

김초은

Fairness Aware Counterfactuals for Subgroups (NeurIPS 2023)

박세현

FairFed: Enabling Group Fairness in Federated Learning (AAAI 2023)

정휘창

A Fair Generative Model Using LeCam Divergence (AAAI 2023)

이경선

Achieving Counterfactual Fairness for Anomaly Detection PAKDD 2023

이지후

Fair Representation Learning for Recommendation: A Mutual Information Perspective (AAAI 2023)

SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification (AAAI 2023)

20240222
Fairness

김초은

• Loss Balancing for Fair Supervised Learning (ICML 2023)

박세현

• Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks (ICML 2023)

정휘창

• Fair Neighbor Embedding (ICML 2023)

정한교

• On the Within-Group Fairness of Screening Classifiers (ICML 2023)

박진원

• Balanced Fair K-Means Clustering (IEEE 2023)
  

20240120
MiniMax

박유하

minimax_1: Preliminaries ~ General strategy

공인성

minimax_1: Le Cam ~ Assouad

박찬무

minimax_2: Information theory ~ Fano

김건웅

minimax_2: Yang \& Barron ~ Devroye \& Lugosi

박석훈

minimax_3: Extension of Le Cam

정휘창

minimax_3: Extension of Assouad

이지후

minimax_3: Extension of Fano ~ Non finite case

최성식

minimax_4: Rate optimality ~ Examples (M-estimator)

박세현 정한교

minimax_4: Examples (Smooth density estimation) ~ Examples (Functional estimation)

이경선 김초은

minimax_4: Asymptotic efficiency ~ Asymptotic equivalence

세미나 자료

Minimax.zip

20240119
Fairness

이경선

• ICLR 2023 FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data

정휘창

• ICML 2023 Differential Privacy has Bounded Impact on Fairness in Classification

김초은

• Adapting fairness interventions to missing values

박진원

• ICML 2023 Approximation Algorithms for Fair Range Clustering

20240108
2023 NeurIPS

이경선

• TabMT: Generating tabular data with masked transformers

이지후

• Flat Seeking Bayesian Neural Networks

김초은

• Tree of Thoughts: Deliberate Problem Solving with Large Language Models

박찬무

• Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions

신윤섭

• Gaussian Process Probes (GPP) for Uncertainty-Aware Probing

박유하

• Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition

박진원

• Students Parrot Their Teachers: Membership Inference on Model Distillation

박석훈

• Feature learning for interpretable , performant decisition treess

정휘창

• Causal normalizing flows: from theory to practice

최성식

• A Measure-Theoretic Axiomatisation of Causality

정한교

• Are Emergent Abilities of Large Language Models a Mirage?

박세현

• Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization

공인성

• A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning

김건웅

• FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-trained Models in Few-Shot Learning

20231219
XAI

조영래

• Dividing and Conquering a BlackBox to a Mixture of Interpretable Models
• Label free explainability for unsupervised model

박찬무

• Explainability as statistical inference
• Learning_Perturbation

김건웅

• Identifying Interpretable Subspaces in Image
• Visual Classification via Description from

박석훈

• Interpretable GAN
• ProtoVAE

최성식

• Inducing Causal Structure for Interpretable Neural Networks
• Neural Basis Models for Interpretability

신윤섭

• Natural Posterior Network Deep Bayesian Uncertainty for Exponential Family Distribution
• Towards Trustworthy Explanation On Causal Rationalization

20230130
Fairness

박석훈

• Fair Bayes-Optimal classifiers under predictive parity

정연호

• Counterfactual Fairness with Partially Known Causal Graph

양동윤

• On the Tradeoff Between Robustness and Fairness

정휘창

• Fair and Efficient Allocations Without Obvious Manipulations

최성식

• Fairness without Demographics through Knowledge Distillation

김건웅

• Domain Adaptation meets Individual Fairness. And they get along
• Optimal Transport of Classifiers to Fairness

박진원

• Transferring Fairness under Distribution Shifts via Fair Consistency Regularization

이지후

• Fairness Reprogramming

박유하

• Fair Ranking with Noisy Protected Attributes

박세현

• Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting

박찬무

• Conformalized Fairness via Quantile Regression
  

이종진

• Self-Supervised Fair Representation Learning without Demographics

김성현

• Fairness Transferability Subject to Bounded Distribution Shift

이경선

• Pushing the limits of fairness impossibility: Who’s the fairest of them all?

20220117
Overparameterization

박유하

• Benign overfitting in linear regression

정휘창

• Benign overfitting in ridge regression

김건웅

• Just interpolate: Kernel “ridgeless” regression can generalize

박진원

• On uniform convergence and low-norm interpolation learning

최성식

• Exact gap between generalization error and uniform convergence in random feature models

이종진

• Uniform convergence of interpolators: Gaussian width, norm bounds, and benign overfitting

공인성

• In defense of uniform convergence: Generalization via derandomization with an application to interpolating predictors

박석훈

• Minimum ℓ1-norm interpolators: Precise asymptotics and multiple descent

이지후

• A random matrix analysis of random fourier features: beyond the gaussian kernel, a precise phase transition, and the corresponding double descent

양동윤

• Generalization of two-layer neural networks: An asymptotic viewpoint
  

20220114

최성식

• Some Methods for Estimating CATE

20210719

공인성

• Approximate function with DNN

20211109

최성식

• Causal paper review

20210719

이종진

• Review of Sparsity

20210716

양동윤

• Review of Fair Clustering Papers

20210520

박석훈

• Implicit regularization in deep matrix factorization

박진원

• Kernel and rich regimes in overparametrized models

이지후

• Implicit bias of gradient descent for wide two-layer neural networks Trained with the Logistic Loss

박유하

• The implicit bias of depth, How incremental learning drives generalization

이종진

• A unifying view on implicit bias in training linear neural networks

20210513

정휘창

• Implicit regularization in matrix factorization

김건웅

• Characterizing Implicit Bias in Terms of Optimization Geometry

최성식

• Implicit Bias of Gradient Descent on Linear Convolutional Networks

양동윤

• Gradient descent aligns the layers of deep linear networks

공인성

• The Implicit Bias of Gradient Descent on Separable Data

20210216

최성식

• Ranking problem

20200907

양동윤

• Class activation map

이종진

• Neural network pruning

20200829

문상준

• Unconstrained convex optimization through first-order approximation methods

20200825

이종진

• Practical Secure Aggregation for Privacy-Preserving Machine Learning (Ch 4, 5)

20200804

김사라

• Individual Fairness AI Reviews

20200729

공인성

• Practical Secure Aggregation for Privacy-Preserving Machine Learning (Ch 3.2, 3.4)

김건웅

• Practical Secure Aggregation for Privacy-Preserving Machine Learning (Ch 3.3, 3.5, and 3.6)
  

20200715

이종진

• Practical Secure Aggregation for Privacy-Preserving Machine Learning

정휘창

• Abstract Algebra

20200702

김사라

• Constrained Fairness AI Reviews

최용찬

• Counterfactual Fairness

20200606

채민우

• Introduction to Hamiltonian Monte Carlo methods

20200516

양동윤

• Review of DeLiGAN:Generative Adversarial Networks for Diversed and Limited Data

최용찬

• VEEGAN : Reducing Mode Collapse in GANs using Implicit Variational Learning

이종진

• Bayesian GAN

김건웅

• Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images

공인성

• Mixture of GANs for Clustering

김성현

• On GANs and GMMs

정휘창

• Mixture Density Generative Adversarial Networks

백규승

• PROBGAN: Towards probabilistic GAN with theoretical guarantees

예성주

• Variational Bayesian GAN

최용찬

• Label-removed generative adversarial networks incorporationg with K-Means

최성식

• A Style-Based Generator Architecture for Generative Adversarial Networks

20200107

이종진

• Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec

백규승

• Learning Sparse DNN Architecture
  

20200103

이상엽

• Adam, AMSGrad, AdamX

20190909

이상엽

• Sparse VAE

백규승

• Generalization error bound – via Compressing Deep Neural Network

최용찬

• Semi-supervised learning with MixUp method – ICT

김사라

• Data Augmentation Reviews

20190708

이상엽

• Approximation and Estimation for High-Dimensional Deep Learning Networks
• Risk Bounds for High-dimensional Ridge Function Combinations Including Neural Networks
• Spectrally-normalized Margin Bounds for Neural Networks

백규승

• On Tighter Generalization Bounds for Deep Neural Networks: CNNs, ResNets, and Beyond
• Size-Independent Sample Complexity of Neural Networks

20190701

서지인

• A Survey of Algorithms and Analysis for Adaptive Online Learning
  

20190603

김보영

• A generalized online mirror descent with applications to classification and regression

김사라

• Adaptive Bound Optimization for Online Convex Optimization

백규승

• Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
• Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization

서지인

• AdaGrad stepsizes : sharp convergence over nonconvex landscapes, from any initialization

20190528

최용찬

• Bayesian clinical trials

김주은

• Bayesian clinical trials
• Bayesian predictive power for interim adaptation in seamless phase II/III trials where the endpoint is survival up to some specied timepoint

공인성

• Use of Bayesian statistics in drug development: Advantages and challenges
• Bayesian design and analysis for clinical trials – a case study and a learner’s perspective

이상엽

• BAYESIAN CLINICAL TRIALS: WHY BOTHER?
• Practical Bayesian Adaptive Randomisation in Clinical Trials

이종진

• A Bayesian adaptive design for clinical trials in rare diseases
• A Bayesian sequential design with adaptive randomization for 2-sided hypothesis test

주여진

• A Bayesian sequential design with binary outcome
• Bayesian apative designs for clinical trials

20190218

이상엽

• SGD 기반 최적화 기법들의 이론적 성질

최용찬

• Explainable AI paper

황재성

• The Solution Of Generalized Lasso For Non Full Rank Case

20190211

온일상

• Sparse variable clustering models

김성현

• Selecting climate change scenarios for regional hydrologic impact studies based on climate extremes indices
  

이종진

• Towards dynamic catchment modelling: a Bayesian Hierarchical mixtures of experts framework

20190125
워크샵

황재성

• Exact Solution Path Algorithm Of Generalized Lasso For Non Full Rank Case

이상엽

• SGD 기반 최적화 기법들의 이론적 성질

김사라

• Outcome uncertainty and attendance demand in sport: the case of English soccer

백규승

• Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
• Attention-based LSTM for Aspect-level Sentiment Classification

김성현

• Attention Networks for Aspect-Level Sentiment Classification

이종진

• Hierarchical Attention Networks for Document Classification
• Semisupervised Autoencoder for Sentiment Analysis

20190115
Recommender seminar with 라인 플러스, TL AI DEV팀

최용찬

• Causal Embeddings for Recommendation

김주은

• Generation Meets Recommendation: Proposing Novel Items for Groups of Users

백규승

• Origin of Native FTRL-Proximal

주여진

• Impact of Item Consumption on Assessment of Recommendations in User Studies

김호철

• Translation-based Factorization Machines for Sequential Recommendation

20181228

김사라

• Reviews of DeepLDA

20181207
Recommender seminar with 라인 플러스, TL AI DEV팀

김보영

• Unbiased Online Recommender Evaluation for Missing-Not-At-Random Implicit Feedback

김사라

• Adaptive Collaborative Topic Modeling for Online Recommendation

서지인

• Spectral Collaborative Filtering
  

유찬우

• Explore, exploit, and explain_ personalizing explainable recommendations with bandits
• HOP-rec_ high-order proximity for implicit recommendation

20181207
Recommender seminar with 라인 플러스, TL AI DEV팀

김보영

• Unbiased Online Recommender Evaluation for Missing-Not-At-Random Implicit Feedback

김사라

• Adaptive Collaborative Topic Modeling for Online Recommendation

서지인

• Spectral Collaborative Filtering
  

유찬우

• Explore, exploit, and explain_ personalizing explainable recommendations with bandits
• HOP-rec_ high-order proximity for implicit recommendation

20181115

이상엽

• Learning AND-OR Templates for Object Recognition and Detection

김주은

• Visualizing and Understanding Convolutional Networks

주여진

• “Why Should I Trust You?” Explaining the Predictions of Any Classifier
  

이종진

• Generating Visual Explanations

김성현

• Attentive Explanations: Justifying Decisions and Pointing to the Evidence

최용찬

• Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning models

김보영

• Interpretable classifiers using rules and bayesian Analysis: building a better stroke prediction model
• Human-level concept learning through probabilistic program induction

20181017

백규승

• Mirror descent, Hedge

이상엽

• Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization

minimax seminar
김경희 교수님

20181006

• Minimax lower bounds I
  

20181013

• Minimax bounds II
  

20181201

• Minimax bounds III
  

20181208

• Minimax bounds IV

20180921

김성현

• Online Incremental Feature Learning with Denoising Autoencoders

이상엽

• Bayesian Incremental Learning for Deep Neural Networks

김사라

• Error-Driven Incremental Learning in Deep Convolutional Neural Network for Larg-Scale Image Classification
  

최용찬

• Evolutive deep models for online learning on data streams with no storage

백규승

• Incremental Training of Deep Convolutional Neural Networks
• Online Deep Learning: Learning Deep Neural Networks on the Fly

20180809

이상엽

• Anomaly Detection: A Survey

이종진

• Outlier Detection for Temporal DATA: A Survey

이지수

• A survey of outlier detection
  

김사라

• Deep autoencoding gaussian mixture model for unsupervised anomaly detection

최용찬

• Semi-Supervised Anomaly Detection

20180726 Recommender seminar with 네무스텍(Nemustech)

윤철환

• AD Click Prediction a View from the Trenches

유찬우

• DEEP NEURAL NETWORKS FOR YOUTUBE RECOMMENDATIONS

이지홍

• Related Pins at Pinterest: The Evolution of a Real-World Recommender System
  

정구환

• Online Learning to Rank in Stochastic Click Models
  

20180705

정구환

• Slice Samplers for DPM
• Hamiltonian Monte Carlo and Langevin Monte Carlo
• Factor Graphs, the Sum-Product Algorithm and TrueSkillTM

백규승

• Neural Network Learning: Theoretical Foundation
• Learning by Mirror Averaging

김동하

• Understanding deeply and improving VAT
  
• Generative Auto Encoder

20180628 Recommender seminar with 네무스텍(Nemustech)

서지인

• Click-through Prediction for Advertising in Twitter Timeline
• Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks

최용찬

• CTR prediction models

20180426

이상엽

• DeepDocClassifier: Document Classification with Deep Convolutional Neural Network

김성현

• Distributed Representations of Sentences and Documents

최용찬

• Document context language models
  
• Document embedding with Paragraph vectors

김사라

• Efficient Vector Representation for Documents through Corruption
• Document Embeddings via Recurrent Language Models

김보영

• Hierarchical Recurrent Neural Network for Document Modeling

김동하

• Document Embeddings

이종진

• Classify of Select: Neural Architectures for Extractive Document Summarization

백규승

• From Word Embeddings To Document Distances
• Hierarchical Deep Learning for Text Classication

정구환

• Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
• SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

최세민

• Multi-Task Learning for Document Ranking and Query Suggestion
• Hierarchical Multiscale Recurrent Neural Networks

20180425 Recommender seminar with 네무스텍(Nemustech)

김호철

• Factorization Machines, Field-aware Factorization Machines

유찬우

• Simple and Scalable Response Prediction for Display Advertising

김보영

• Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine
  
• adPredictor and Message Passing

이종진

• Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising

김사라

• Personalized click prediction in sponsored search

20180202

정구환

• Wide and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning

김사라

• PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs

서지인

• Hierarchical Attentive Recurrent Tracking(HART)
  

최세민

• Dilated Recurrent Neural Networks

온일상

• MaskRNN: Instance level video object segmentation

이종진

• Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

백규승

• Z-Forcing: Training Stochastic Recurrent Networks

김보영

• Gated Recurrent Convolution Neural Network for OCR

최용찬

• Learning Hierarchical Information Flow with Recurrent Neural Modules

김동하

• Cortical microcircuits as gated-recurrent neural networks

Recommender seminar with 네무스텍(Nemustech)

논문

• Deep Learning based Recommender System: A Survey and New Perspectives

20171122
이종진

• Multilayer Perceptron based Recommender System

20171122
김보영

• Autoencoder based Recommendation System
  

20171128
서지인

• Convolutional Neural Network based Recommender System

20171226
최용찬

• Recurrent Neural Network based Recommender System

20171226
이지수

• Learning deep structured semantic models for web search using clickthrough data

20180124
정구환

• Restricted Boltzmann Machine based Recommender System

20180124 유찬우

• NADE, GAN Recommender

20180108

최세민

• Introduction to GAN variants

20171129

최세민

• Capsule Networks

20171021

온일상

• Mixture models with a prior on the number of components

백규승

• On the Expressive Power of Deep Neural Networks

김사라

• Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
  

김보영

• Instacart 장바구니 자료 분석

20170731

정구환

• Recommendation with Non-Random Missing Data

20170722

김동하

• Deep Decision Network for Multi-Class Image Classification

최세민

• Generative Adversarial Nets

20170714

김동하

• Supervised learning exploiting clustering information

온일상

• Adaptive Face Verication
  

최세민

• Introduction to face verification
  

20170708

백규승

• Learnable pooling with context Gating for video classification

20170703

김동하

• Neural Machine Translation, Answer Sentence Selection

최세민

• Deep Learning Models for Information Retrieval

김보영

• Next Basket Recommendation
  

황참이

• Generalized Additive Model

전종준

• Primal path algorithm for compositional data analysis

20170519

김보영

• Google Cloud & YouTube-8M Video Understanding Challenge

*회의록

• 20170519 회의록

20170511

김성현

• Google Cloud & YouTube-8M Video Understanding Challenge

백규승

• Exploratory data analysis of labels

20170504

김사라

• YouTube-8M:A Large-Scale Video Classification Benchmark
• YouTube-8M:A Large-Scale Video Classification

김성현

• Google Cloud & YouTube-8M Video Understanding Challenge

김보영

• Learning without forgetting
  

백규승

• Saliency Detection by Multi-Context Deep Learning
• Exploratory data analysis of labels

최세민

• Hierarchical Multi-Label Classification Using Local Neural Networks

최용찬

• ImageNet Classication with Deep Convolutional Neural Networks
  

20170414 네트워크 시각화

황참이

• Glossary of terms

김성현

• Network Cohesion & Graph Partitioning

최용찬

• 자료와 코드
  

20170303 GS-Shop

김동하

• 딥러닝에서의 준 지도 학습법에 관한 연구

최세민

• Personalized Regression for Recommender System

김보영

• Recommender System and Data Analysis
  

정구환

• 토픽모형을 이용한 마이크로 세그멘테이션

온일상

• Sparse Bayesian factor analysis

20170110

김보영

• Autoencoder for Collaborative Filtering
• A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines

김동하

• Some discovery of deep learning

김성현

• DNN & visualization in R
  

온일상

• Quantifying lagged effects with a fused lasso penalty
• Sparse factor models with IBP prior

정구환

• Hamiltonian Monte Carlo
• Trueskill

김사라

• Self Consistency

최세민

• Sparse CCA via Precision Adjusted Iterative Thresholding

조성현

• Comparison of Ridge Penalized Logistic Model and Ridge Penalized Simple Linear model in Dichotomous response

백규승

• Wavelets and high order numerical differentiation

20161203

고세윤

• Active Learning

김보영

• Content-Aware Point of Interest Recommendation on Location-Based Social Networks
• Restricted Boltzmann Machines for Collaborative Filtering

김사라

• Explaining Nonlinear Classification Decisions with Deep Taylor Decomposition
  

정구환

• Expectation Propagation
• Undirected Topic Models

온일상

• Measuring Invariances in Deep Networks

권용찬

• 3D Multiscale Residual U-Net Architecture for Brain Lesion Segmentation

최세민

• Semi-supervised learning and clustering using deep structure literature review
• Variants of Canonical Correlation Analysis

백규승

• Visualizing and Understanding Convolutional Networks

김성현

• Visualizing Higher-Layer Features of a Deep Network