[20210613] Weekly AI ArXiv 만담

[jungwoo-ha]

• AI News

  • ICCV 2021 review가 나왔어요. Rebuttal 하시는 분들 모두 파이팅!
  • ICASSP 2021: 6월 6일 ~ 11일
  • CVPR 2021: 6월 19일 ~ 25일
  • Best paper candidate: http://cvpr2021.thecvf.com/node/290 (고려대 김창수 교수님 연구실)
  • 한국 스폰서쉽: 네이버, LG AI Research, SuperbAI,
  • 미정부, AI 연구에 정부 핵심 공공데이터 공유 TF 발족 (https://n.news.naver.com/article/001/0012453713)
  • 백악관 뉴스는: https://www.whitehouse.gov/ostp/news-updates/2021/06/10/the-biden-administration-launches-the-national-artificial-intelligence-research-resource-task-force/
  • RL로 차세대 AI 칩 설계: https://n.news.naver.com/article/293/0000035052
  • 원격의료 규제 과연 풀릴까: http://www.docdocdoc.co.kr/news/articleView.html?idxno=2011552
  • 프랑스 격리 해제: https://news.naver.com/main/read.nhn?mode=LSD&mid=shm&sid1=103&oid=421&aid=0005399038

• Arxiv

  • Scaling Vision Transformers
  • ViT를 정말 열심히 키워봤어요
  • 그냥 키우면 이래저래 힘드니 온갖 다양한 트릭들 (LR, WD, 메모리 절약형 Adam 등등등)
  • 데이터도 모자라서 pseudo-labeling style semi-supervised 로 JFT-300M --> JFT-3B로 뻥튀기도 했어요
  • 그랬더니 GPT-3에서 보여주던 Scaling law 가 보여요.
  • ImageNet-1k SOTA도 깼어요 (Finetuning 기준) - Meta pseudo labeling 기록 90.2
  • 근대 ViT-G/14 정도는 되어야 쓸만 한 것 같아요..
  • 역시 우리는 기업과 학교가 함께 고민해야 겠어요.
  • NWT: Towards natural audio-to-video generation with representation learning
  • Audio-to-video 연구에요.
  • 여기도 Discrete VAE with Wasserstein adversarial loss를 사용합니다. 멀티모달은 뭔가 discrete embedding이 굉장히 잘 워킹하는 듯
  • 제목과는 다르게 퀄이 생각보다 좋습니다.
  • Frame-level AR < Memcode-level AR
  • https://next-week-tonight.github.io/NWT/
  • Ack에 Alex가....
  • Barbershop: GAN-based Image Compositing using Segmentation Masks
  • https://zpdesu.github.io/Barbershop/
  • Structure (Semantic mask), Appearance, Identity --> 얼굴, 머리모양, 머리색 조합
  • Swarm Learning for decentralized and confidential clinical machine learning
  • 네이처(자매지 아니고)에 소개된 탈중앙화 privacy-preserving ML
  • Decision Transformer: Reinforcement Learning via Sequence Modeling
  • RL을 value function, policy gradient 없이... LM 스타일로..
  • Cross-Modal Discrete Representation Learning
  • from MIT
  • visual object 와 spoken word의 concept과 event 같은 멀티모달을 fine granularity 로 표현하는
  • 이걸 discrete embedding 파트를 쉐어한다고.. (Cross-modal code matching)
  • fine-grained (pixel, word, frame 등)이 video, sentence, waveform등과 complementary 역할을 한다고..
  • Automated Self-Supervised Learning for Graphs
    • GNN에서 SSL이 대세이긴한데 pretext task들이 매우 다양함
    • target task와 dataset에 따라 pretext task 선택이 중요하더라는
    • 이걸 Auto로 자동으로 여러개 pretext task를 활용하는지 (ES, DS) 둘다 제안. 기본 철학은 homophily
    • https://github.com/ChandlerBang/AutoSSL (아직코드는 공개전)
  • Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning
  • 다양한 공개 얼굴데이터셋을 활용하여 마스크를 씌우고 공개한 데이터셋
    
  • https://github.com/sachith500/ContrastiveFaceRepresentation
  • MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training
  • Midi 같은 symbolic 데이터를 LM 스타일로 pretraining (100만곡) (from MSRA, MS, 북경대, Findings of ACL 2021)
  • 그냥하면 당연히 안되서 OctupleMIDI 인코딩, bar-level masking 등 다양한 아이디어를 넣었다고 함.
  • Melody completion, 반주제안, 장르 분류, 스타일 분류 등 downstream task
    
  • MST: Masked Self-Supervised Transformer for Visual Representation
  • Teacher-student 구조를 기반으로 attention guided token masking 으로 self-supervised learning 효율 높임.
  • Student에선 decoder (CNN)도 붙여서 reconstruction도 수행하면서 local 정보도 보존하도록
  • 결론은 DINO랑 마지널 하게 좋은데 (classification도 detection, segmentation도) epoch수는 덜먹음 (CNN upsample decoding recon이 multitask라.. 실제 수행시간은 어떨지??).
  • 백본은 DeiT-S랑 Swin-T 를 사용해서 비교
  • Ruddit: Norms of Offensiveness for English Reddit Comments
  • NSMC의 Reddit 버전? ㅎㅎ, -1부터 1점까지 레딧 댓글 정량화한 데이터셋 (ACL2021)
  • https://github.com/hadarishav/Ruddit

[veritas9872]

ICML 2021 Accepted Papers (Initial) ICML 2021 페이퍼들이 나왔습니다. 다음 주 중으로 재미있어 보이는 논문 선별해 공유해드리도록 하겠습니다. https://icml.cc/Conferences/2021/AcceptedPapersInitial

CALM (feature attribution) 네이버에서 새로 나온 연구 제가 하정우님 대신해서 올려드립니다 ㅋㅋㅋ https://github.com/naver-ai/calm 아직 Arxiv에 논문은 찾지 못했지만 기존의 GradCAM의 단점을 극복할 수 있는 deep learning attrribution 연구를 기대하겠습니다.

Single Image Depth Estimation using Wavelet Decomposition (CVPR 2021) https://arxiv.org/abs/2106.02022 추억팔이 느낌으로 공유해드립니다. 대부분 비전 딥러닝은 이미지 영역에서 (아직까지는) CNN을 사용해서 학습을 진행하는데 Wavelet transform 혹은 Fourier Transform으로 다른 feature representation으로 바꾸어 학습하는 것을 생각해볼 수 있습니다. 여기에서는 사용되지 않았지만 예를 들어 Fourier Transform으로 변환한 후에 complex-valued transformer를 사용해보는 방법 또한 생각해볼 수 있습니다. 마침 PyTorch 1.8.1부터 복소수 학습 지원이 추가되었는데 혹시나 연구 주제가 필요하신 분들은 참조하시기를 ㅎㅎ

[jshin49]

• Shades of BLEU, Flavours of Success: The Case of MultiWOZ
  • Reports yet another problem with MultiWOZ
  • "unsatisfactory preprocessing, insufficient or underspecified evaluation metrics, or rigid database"
  • show that reported scores cannot be directly compared
  • release standalone standardized evaluation scripts
• AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation
  • novel data augmentation approach that combines a self-trained neural retrieval model with a few-shot learned NLU model
  • outperforms the state-ofthe-art methods on the FEWSHOTWOZ data in both BLEU and Slot Error Rate
• VARIATIONAL INFORMATION BOTTLENECK FOR EFFECTIVE LOW-RESOURCE FINE-TUNING
  • ICLR 2021
  • fine tune a pretrained BERT Model using Variational Information Bottleneck method of Alemi to improve transfer learning in low resource scenarios
  • improvements and the success on a wide range of tasks and the surprising success of VIB over other alternatives like dropout
  • 원래 tie 였는데 AC 가 위와 같은 메타리뷰로 억셉시켰음
• AGGGEN: Ordering and Aggregating while Generating
  • ACL 2021
  • performs sentence planning at the same time as generating text by learning latent alignments (via semantic facts)
  • compared with vanilla seq2seq?

[nick-jhlee]

• Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks

  • Bogazici University, Telecom Paris, INRIA, Vector Institute
  • 최근 neural network를 compress하는 기법들이 많이 나오고 있는데, 다 대충 잘 되는 것 같애요
  • 이 논문: Why do they work??
  • Theoretical results
    • Suppose that both theories are true simultaneously: heavy tail theory of SGD & mean-field regime of neural network
    • Then networks are l_p-compressible!
    • magnitude pruning, singular value pruning, node pruning works, provably!
    • novel error and generalization bound for compressible networks!

• Regularization in ResNet with Stochastic Depth

  • University of Oxford, Huawei Technologies at France
  • ECCV 2016에서 depth가 stochastic하게 바꾸는 approach가 있었음! (Huang et al., 2016)
  • For each mini-batch, drop residual blocks with some probability by bypassing it with identity mapping!
  • 실제로 training time 엄청 줄이고 test accuracy가 많이 증가했음!
  • 이 논문: Why does it work??
  • Theoretical results
    • Enforces flatness on loss surface as an explicit regulariser
    • Explicit quantification of "best" survival probability
    • large depth에선 Gaussian noise injection과 비슷한 효과!
    • 기타 등등

• E(n) Equivariant Graph Neural Networks (ICML 2021)

  • University of Amsterdam (Max Welling 교수님 참여...!)
  • point clouds, molecular structure, n-body particle simulation: translation, rotation symmetries!! => E(n)-equivariant...
  • 기존의 방법론보다 computational overhead가 훨씬 덜 함! + E(n), n>2도 가뿐히 할 수 있음!
  • dynamical system, graph autoencoder, molecular property prediction에서 기존의 방법들 다 이김! + 더 빠름!

• Barlow Twins: Self-Supervised Learning via Redundancy Reduction (ICML 2021)

  • FAIR, NYU (LeCun 아저씨 있음!)
  • 기존 SSL 방법론들은 trivial solution을 avoid하려고 많은 짓(?)을 함
  • Barlow Twins!
  • neuroscience의 "redundancy-reduction principle"에서 inspired
  • 방법론: "measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample, and making it as close to the identity matrix as possible."
  • trivial sol 절대 안나옴: "the representation vectors of distorted versions of a sample to be similar, while minimizing the redundancy between the components of these vectors."
  • negative-sample-free method
  • related to information bottleneck principle
  • semi-supervised low-data regime 새로운 SOTA, ImageNet/transfer classification/objective SOTA랑 비슷한 성능

• Graph Barlow Twins: A self-supervised representation learning framework for graphs

  • Wroclaw University of Science and Technology, University of Notre Dame
  • 위에 논문을 graph에 적용!
  • Hilbert-Schmidt Independence Criterion (HSIC)과 연관이 있음!
  • Significantly faster convergence, comparable performance to SOTA

• Chasing Sparsity in Vision Transformers: An End-to-End Exploration

  • University of Texas at Austin, Microsoft
  • 가장 이상적인 pruning!
  • end-to-end training!
  • low training memory overhead
  • no accuracy loss
  • "Specifically, instead of training full ViTs, we dynamically extract and train sparse subnetworks, while sticking to a fixed small parameter budget. Our approach jointly optimizes model parameters and explores connectivity throughout training, ending up with one sparse network as the final output."
  • "our sparsified DeiT-Small at (5%, 50%) sparsity for (data, architecture), improves 0.28% top-1 accuracy, and meanwhile enjoys 49.32% FLOPs and 4.40% running time savings

• Implicit Representations of Meaning in Neural Language Models (ACL 2021)

  • MIT
  • Main question: Just about how much of meaning is represented by NLM??
  • "in simple semantic domains, they build representations of situations and entities that encode logical descriptions of each entity’s dynamic state."
  • Empirically validate this claim!
  • 기존의 "probing": predict semantic roles from NLM embeddings
  • 여기서 제안한 "probing": recover a representation of the situation described by a discourse
  • 검증하려는 가설: LMs represent (a particular class of) information states
  • 정확한 실험 세팅은 논문 참조! (<- 여기에 담기엔 너무 복잡,,,)
  • Future direction: improving factuality and coherence, correct biases...etc.
    

[jwlee-ml]

위 RL 이용한 chip design 관련 link 입니다. https://www.nature.com/articles/d41586-021-01515-9?fbclid=IwAR2m-A7IbIWAMQiddsAUJ_v6R2TCz5arnfBwbnRzUzBAB0dQClNmP5BUHaU

[jshin49]

stochastic depth on Transformers

Reducing Transformer Depth on Demand with Structured Dropout

• ICLR 2020