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7 Papers & Radios _ Southern California game AI has "Doom"; overview of the small sample NLP meta-learning

Center of the Machine & ArXiv Weekly Radiostation Participation: Du Wei, Chu Hang, Luo Ruotian This week's important papers are the AI ??agent for playing the Doom game produced by USC, and a small-sample NLP meta-studying review by Salesforce researchers. table of Contents:

* Stabilizing Differentiable Architecture Lookup via Perturbation-based Regularization

* Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

* Searching to Exploit Memorization Effect in Learning with Noisy Labels

* Meta-studying for Few-shot Normal Language Processing: The Survey

* Towards Deeper Graph Neural Networks

* Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog

* A Knowledge-Enhanced Suggestion Design with Attribute-Level Co-Attention

* ArXiv Weekly Radiostation: NLP, CV, ML and more determined papers (with sound) Document 1: Stabilizing Differentiable Architecture Lookup via Perturbation-based Regularization * Author: Xiangning Chen, Cho-Jui Hsieh *Thesis link: Abstract: Recently, the microarchitecture research algorithm has shortened the NAS research time to several days, which includes attracted much interest. However, its ability to stably generate high-performance neural networks has been widely questioned. Many researchers have discovered that as the research progresses, the system architecture generated by DARTS will get worse and worse, and eventually even completely skip connections. In order to support gradient descent, DARTS has made a continuing approximation of the search space and is always optimizing a couple of constant differentiable frame weights A. However when generating the final frame, this fat needs to be discretized. In this article, the research authors from the University of California, Los Angeles observed that losing function of this set of continuous frame weights A on the validation collection is quite unsmooth, and DARTS constantly converges to an extremely sharp area. For that reason, hook disturbance to A will greatly reduce the efficiency of the verification fixed, not to mention the final discretization process. Such a sharp loss function may also impair the exploration ability of the research algorithm in the architecture area. For that reason, they proposed a fresh NAS framework SmoothDARTS (SDARTS), which makes losing function of The in the verification set very smooth.

The verification accuracy of the architecture weight A on CIFAR-10.

SDARTS instruction algorithm.

Compare the test error effects with some other SOTA classifiers on ImageNet. Recommendation: The method proposed in this article could be widely applied to various differentiable architecture algorithms. On numerous data units and search areas, the researchers discovered that SDARTS can consistently achieve performance enhancements. Document 2: Sample Factory: Egocentric 3D Control from Pixels with 100000 FPS with Asynchronous Reinforcement Learning * Author: Aleksei Petrenko, Zhehui Huang, Tushar Kumar, Gaurav Sukhatme, Vladlen Koltun *Thesis link: Abstract: Recently, a study group from the University of Southern California and Intel Labs created a fresh method that may train serious reinforcement studying algorithms on common hardware inside academic laboratories. The study was approved by the ICML 2020 conference. In this study, the researchers showed how to use a single high-finish workstation to train an AI with SOTA performance in the first-person shooter video game Doom. Not just that, they utilized a small part of their normal computing capacity to solve 30 different 3D challenge packages created by DeepMind. In the specific configuration, the researchers used a workstation-class PC with a 10-core CPU and GTX 1080 Ti GPU, and a system built with a server-class 36-core CPU and a single RTX 2080 Ti GPU.

Architecture diagram of Sample Factory.

Hardware system 1 and system 2.

In the three simulation conditions of Atari, VizDoom and DMLab, Sample Factory is closer to the ideal performance than the baseline methods such as DeepMind IMPALA, RLlib IMPALA, SeedRL V-trace and rlpyt PPO. Recommendation: Complete abuse of the "robot", 36-primary CPU stand-alone environment, and the game AI of Southern University to achieve SOTA performance inside Doom. Document 3: Searching to Exploit Memorization Effect in Learning with Noisy Labels * Author: Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James T. Kwok *Thesis link: Abstract: Sample choice is really a common method for robust studying of noise tags. However, how to properly handle the selection process so the deep system can take advantage of the memory impact is really a big problem. In this study, inspired by AutoML, researchers from the Fourth Normal Form, Tsinghua University along with other institutions modeled this issue as a function approximation issue. Specifically, they created a domain-specific search space in line with the general pattern of the memory impact, and proposed a fresh Newton algorithm to efficiently solve the double-layer optimization issue. In addition, the researcher additional conducted a theoretical analysis of the algorithm to make sure a good approximation of the vital point. Experimental outcomes on benchmark and real-world data units show that this method is superior to the existing optimal noise label learning technique and is more efficient than the present AutoML algorithm.

On CIFAR-10, CIFAR-100 and MNIST, use instruction and test accuracy curves under different architectures, optimizers and optimizer settings.

Algorithm 2.

The change curve of label precision (lable precision) of MentorNet, Co-teaching, Co-teaching + and S2E on MNIST. Suggestion: Hansi Yang, the next papers of the thesis, can be an undergraduate of Tsinghua University and is currently an intern inside the fourth paradigm device learning research team. Document 4: Meta-studying for Few-shot Normal Language Processing: The Survey * Author: Wenpeng Yin *Thesis link: Abstract: In this article, researchers from Salesforce offer an overview of meta-learning inside small-sample natural vocabulary processing. Particularly, this article strives to supply a clearer definition of the use of meta-studying in small-sample NLP, summarizes new developments, and analyzes some commonly used data sets.

Multi-task studying VS meta-learning.

Reptile (OpenAI) meta-studying (batched version).

Some representative optimization-based meta-studying models. Recommendation: The author of the papers Wenpeng Yin (Wenpeng Yin) is currently a Salesforce research scientist. He was the chairperson of NAACL 2019 and ACL 2019. Document 5: Towards Deeper Graph Neural Networks * Author: Meng Liu, Hongyang Gao, Shuiwang Ji *Thesis link: Abstract: Inside this study, researchers from Texas The &M; University submit a number of brand-new insights on the development of deeper graph neural networks. They first conducted a systematic evaluation of this problem and considered that the entanglement between transformation and propagation in today's graph convolution operation is a key factor that significantly reduces the efficiency of the algorithm. For that reason, after decoupling both of these procedures, a deeper graph neural system may be used to understand graph node representations from the larger receptive domain. In addition, predicated on theoretical and empirical analysis, the researchers proposed the Heavy Adaptive Graph Neural Network (DAGNN) to achieve adaptive integration to adaptively integrate information from the large acceptance domain. Experiments on citing, co-authorship and co-purchase data units confirmed the researchers' evaluation and insights and demonstrated the superiority of these proposed methods.

The study proposes the architecture diagram of the DAGNN model.

Comparing the classification accuracy outcomes of various models on the co-authored plus co-purchased data pieces, it could be noticed that DAGNN offers achieved SOTA effects.

On different information sets, the test accuracy change curve of DAGNN at different depths. Recommendation: This short article has been approved by the KDD 2020 conference. Document 6: Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog * Author: Libo Qin, Xiao Xu, Wanxiang Che, Yue Zhang, Ting Liu *Thesis link: Abstract: In this article, researchers from Harbin Institute of Technology and West Lake University propose the shared-private system to learn shared and particular knowledge. Not just that, they also proposed a novel dynamic fusion system (Dynamic Fusion Network, DFNet), that may automatically make use of the correlation between your focus on domain and each domain. Experimental results show that this model is superior to the prevailing methods in the field of multi-domain dialogue and achieves SOTA performance. Finally, even if working out data is small, the design is 13.9% greater than the prior best model normally, showing its good transferability.

Multi-domain dialogue method.

Workflow of benchmark design, share-private design and dynamic fusion design.

Comparison of the main outcomes between SMD and Multi-WOZ 2.1. Recommendation: This short article has been approved by the ACL 2020 conference. Paper 7: The Knowledge-Enhanced Recommendation Design with Attribute-Level Co-Attention ** ** * Author: Deqing Yang, Zengcun Tune, Lvxin Xue, Yanghua Xiao *Thesis link: Abstract: The prevailing recommendation model in line with the attention mechanism has some room for improvement. Several models only use coarse-grained interest mechanisms when generating user representations. Although several improved versions add product attribute (feature) details to the attention module, that is, they incorporate item-related knowledge, they are still An individual indicated that the attention mechanism was applied on this end. In response to these problems, researchers from Fudan University are suffering from a serious recommendation model that uses the (item) attribute-levels attention mechanism on the user representation side and that representation side to cooperate, referred to as ACAM (Attribute-levels Co-Attention Model).

Design architecture diagram.

Performance comparison outcomes on both recommended duties of Douban movie and NetEase track. Recommendation: The design uses a multi-task studying framework to train the loss function, and incorporates understanding (embedding) to represent the learning goal, in order that it can learn better items and product attribute representations. ArXiv Weekly Radiostation The Heart of the Machine and ArXiv Weekly Radiostation, initiated by Chu Hang and Luo Ruotian, selected even more important papers this week on the basis of 7 Papers, including 10 selected papers in the fields of NLP, CV, and ML, and provided papers in audio format. Brief introduction, details are the following: The 10 selected NLP papers this week are: 1. Analogical Reasoning for Visually Grounded Vocabulary Acquisition. (from Shih-Fu Chang) 2. A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation. (from Jiebo Luo) 3. Connecting Embeddings for Understanding Graph Entity Typing. (from Kang Liu) 4. Ramifications of Language Relatedness for Cross-lingual Move Learning in Character-Based Language Versions. (from Mikko Kurimo) 5. Better Earlier than Late: Fusing Subjects with Phrase Embeddings for Neural Question Paraphrase Identification. (from Maria Liakata) 6. XD at SemEval-2020 Task 12: Ensemble Method of Offensive Vocabulary Identification in Social Media Using Transformer Encoders. (from Jinho D. Choi) 7. Will Your Forthcoming Guide achieve success? Predicting Book Achievement with CNN and Readability Ratings. (from Aminul Islam) 8. To End up being or Not To become a Verbal Multiword Expression: A Quest for Discriminating Functions. (from Carlos Ramisch) 9. IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals. (from Shashank Gupta) 10. BAKSA at SemEval-2020 Task 9: Bolstering CNN with Self-Interest for Sentiment Analysis of Code Mixed Textual content. (from Ashutosh Modi) The 10 CV selected papers this week are: 1. CrossTransformers: spatially-aware few-shot exchange. (from Andrew Zisserman) 2. Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval. (from Andrew Zisserman) 3. BSL-1K: Scaling up co-articulated sign vocabulary recognition making use of mouthing cues. (from Andrew Zisserman) 4. Form and Viewpoint without Keypoints. (from Jitendra Malik) 5. NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Lookup. (from Kalyanmoy Deb, Wolfgang Banzhaf) 6. BorderDet: Border Function for Dense Object Recognition. (from Jian Sunlight) 7. WeightNet: Revisiting the look Space of Pounds Systems. (from Xiangyu Zhang, Jian Sun) 8. Funnel Activation for Visual Acknowledgement. (from Xiangyu Zhang, Jian Sun) 9. Uncertainty-Aware Weakly Supervised Actions Recognition from Untrimmed Videos. (from Cordelia Schmid) 10. Vision-based Estimation of MDS-UPDRS Gait Ratings for Assessing Parkinson's Disease Electric motor Intensity. (from Li Fei-Fei) The 10 selected ML papers this week are: 1. Debiasing Concept Bottleneck Versions with Instrumental Variables. (from David E. Heckerman) 2. Interpretable Neuroevolutionary Versions for Learning Non-Differentiable Functions and Applications. (from Marin Solja?i?) 3. Storage Fit Learning with Function Evolvable Streams. (from Zhi-Hua Zhou) 4. PackIt: A Virtual Atmosphere for Geometric Planning. (from Jia Deng) 5. Automated Recognition and Forecasting of COVID-19 making use of Deep Learning Strategies: AN ASSESSMENT. (from Saeid Nahavandi, U. Rajendra Acharya, Dipti Srinivasan) 6. ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Suggestion. (from Boi Faltings) 7. Hybrid Discriminative-Generative Education via Contrastive Learning. (from Pieter Abbeel) 8. Graph Neural Systems with Haar Transform-Based Convolution and Pooling: A Comprehensive Tutorial. (from Ming Li) 9. The most important thing about the No Free Lunch time theorems?. (from David H. Wolpert) 10. Bridging the Imitation Gap by Adaptive Insubordination. (from Svetlana Lazebnik)

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