Out-of-distribution detection using union of 1-dimensional subspaces
2022.11.24
김태욱
NET-DNF Tabular 데이터에 알맞는 Inductive bias로써, DNNF block을 제안함
2022.11.17
한민석
Topic: LIME & SHAP Keywords: Explanable AI Model-agnostic local surrogate model Shapley value, LIME, Reference: 1. Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "" Why should …
2022-11-17
김현호
Topic: Incorporating Prior Domain Knowledge into Loss Function Keywords: Noisy Data Domain Knowledge Neural Networks Deep Learning Limited Training Data Reference: N. Muralidhar, M. R. Is…
2022.11.03
김주헌
Bayesian Linear Regression and Gaussian process regression - full derivation of Bayesian LR - GPR tutorial
2022.10.06
On Mixup Training: Improved Calibration and Predictive Uncertainty for deep Neural Networks Keyword : Calibration, Uncertainty, mix-up
Topic: Certified Monotonic Neural Networks Keywords: interpretable deep learning, Model generalization Neural network, Monotonicity, Reference: Liu, Xingchao, et al. "Certified monotonic…
2022-09-29
Keywords: pruning, initialization, subspace 적은 수의 파라미터를 가진 pruned model이 덜 학습하여도 original network 보다 좋은 performance를 보여줄 수 있다.
2022-06-23
이주형
Topic: How Domain Adaptation Works Keywords: Domain adaptation Transfer learning Learning theory Sample-selection bias Reference: S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Perei…
2022.06.23
Importance Estimation for Neural Network Pruning
2022.06.09
김태흥
1. Discriminative feature space embedding using angular margin loss 2. Separate Maximum softmax probability score between ID and OOD using weight decay regularization keywords : Out-of-distributio…
05/26
Adjusting existing models for regional drift VFDT / CVFDT - A decision tree induction system capable of learning from high-speed data streams in an incremental, anytime fashion. - Extension t…
2022-05-26
Topic: Neural Additive Models: Interpretable Machine Learning with Neural Nets Keywords: generalized additive model, interpretable deep learning, Neural network, Reference: Agarwal, Ris…
2022-05-18
Keywords: data shift, data augmentation, robustness, uncertainty 1. 데이터의 작은 변화에도 모델의 예측 결과는 크게 변화 (Data Shift) 2. 다양한 augmentation 기법을 연쇄적으로 적용하고 mixup 3. 모델의 clean accuracy, robustness, uncerta…
2022-05-12
CLeaR: An adaptive continual learning framework for regression tasks Regression 문제에 대한 New Task / Catastrophic forgetting의 정의 이를 해결하기 위한 Framework 제안
2022-04-28
