The Best of Applied Artificial Intelligence, Machine Learning, Automation, Bots, Chatbots. We iterate over the number of epochs we want to train for. Finally, for online decision making (with bandits and reinforcement learning), we aim to resolve both the conceptual and practical learning, evaluation and deployment challenges by introducing powerful tools from robust optimization and optimal control. --color-link-text-hover-theme:#737373; Use AutoCAD computer-aided design software to create precise 2D and 3D drawings. In this tutorial we will walk you through leading open source tools for glassbox learning, and show how intelligible machine learning helps practitioners uncover flaws in their datasets, discover new science, and build models that are more fair and robust. We will use the test data loader while testing the model after training. We do, however, provide recordings of each TensorFlow coursesession you attend for your future reference. Besides, limited by the lack of information fusion between the two towers, the model learns insufficiently to represent users' preferences on various tag topics well. In the meantime, TAG offers a concept graph which interconnects these fine-grained concepts and entities to provide contextual information. Company history. Inspired by meta-learning that learns new concepts fast with limited training examples, this paper studies data-efficient training strategies for analyzing brain connectomes in a cross-dataset setting. We infuse copper directly into the fibers Recently BERT has achieved significant progress on many NLP tasks including text matching, and it is of great value but also big challenge to deploy BERT to the e-commerce relevance task. Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in developed countries. The PyTorch implementation of this paper can be found here and here. Once abnormal KPI values are observed, root cause analysis (RCA) can be applied to identify the reasons for anomalies, so that we can troubleshoot quickly. Thanks for sharing such a piece of beautiful information, Thank you for sharing, please visit Extensive experiments are conducted on a synthetic dataset and a large-scaled production dataset from the E-commerce voucher distribution business. The lecturers are experts and share their knowledge energetically. The results demonstrate the superiority of STZINB-GNN over benchmark models, especially under high spatial-temporal resolutions, because of its high accuracy, tight confidence intervals, and interpretable parameters.
Deep Learning Course with On the other hand, the capability of making high-CTR retrieval is optimized by learning to discriminate user's clicked ads from the entire corpus. Our code has been developed in PyTorch and open-sourced. Specifically, we propose GraphDNA---a model that builds Graph neural networks (GNNs) into Dynamic Network Anomaly detection. Scroll down to the Select edition section at the bottom of the page. We show that this reliance on CNNs is not necessary and a pure transformer can perform very well on image classification tasks when applied directly to sequences of image patches. The Simplilearn Data Scientist Masters Program is an awesome course! From villains like Killer Croc, Bane, and Brainiac, to Batman. However, most brain network datasets are limited in sample sizes due to the relatively high cost of data collection, which hinders the deep learning models from sufficient training. Lets check out the points that we will cover in this tutorial. SpeedyFeed leads to more than 100x acceleration of the training process, which enables big models to be trained efficiently and effectively over massive user data. Analyzing the few-shot properties of Vision Transformer. Embedding based retrieval (EBR) is a fundamental building block in many web applications. To overcome this issue, we propose a meta controller to dynamically manage the collaboration between the on-device recommender and the cloud-based recommender, and introduce a novel efficient sample construction from the causal perspective to solve the dataset absence issue of meta controller. In particular, it achieves an accuracy of 88.36% on ImageNet, 90.77% on ImageNet-ReaL, 94.55% on CIFAR-100, and 77.16% on the VTAB suite of 19 tasks.
Nms rare living ship - udf.vseua.info But how to save the best weights in PyTorch while training a deep learning model? Youll master deep learning concepts and models using Keras and TensorFlow frameworks through this TensorFlow course. We will focus on two main topics of responsible RSs: (1) developing reliable and trustworthy RS models and algorithms, to provide reliable recommendation results when facing a complex, uncertain and dynamic scenario; (2) assessing the social influence of RSs on human's recognition and behaviours and ensuring the influence is positive to the society. The experiments confirm that AdaBelief combines fast convergence of adaptive methods, good generalizability of the SGD family, and high stability in the training of GANs. Concerned about the threat that so Results demonstrate our model outperforms state-of-the-art baselines such as ST-GNN, MPNN, and GraphLSTM.
pytorch Overall I really enjoyed the training a lot. The pre-training is conducted in an efficient manner with only two forward/backward updates for the combined 14 tasks. AMRule adaptively discovers labeling rules from large-error instances via a boosting-style strategy, the high-quality rules can remedy the current model's weak spots and refine the model iteratively. Demonstrating, with a series of experiments, that. This Deep Learning course with Keras and TensorFlow certification training will give you a complete overview of Deep Learning concepts, enough to prepare you to excel in your next role as a Deep Learning Engineer. The 5th AIoT workshop will be hosted in person in conjunction with the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2022). 1. Unfortunately, conventional machine learning tools and libraries are incapable of efficiently and accurately tackling large-scale output spaces. Showcase skills - The TensorFlow certification is a testament to the fact that you are well-learned about it, which also is a proof of your skills. Vision Transformer pre-trained on the JFT300M dataset matches or outperforms ResNet-based baselines while requiring substantially less computational resources to pre-train. Interaction-based models, although can achieve better performance, are mostly time-consuming and hard to be deployed online. For the first time, the album of the year category was dominated by female acts, with four of the. Both automatic and human evaluations are performed, and the results show the effectiveness of our proposed approach. Specifically, it outperforms GAT by 1.3% regarding predictive accuracy on our large-scale Tencent Video dataset while achieving up to 50x training speedup. Learning Differential Operators for Interpretable Time Series Modeling, ML4S: Learning Causal Skeleton from Vicinal Graphs, Non-stationary Time-aware Kernelized Attention for Temporal Event Prediction, CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation, Discovering Invariant and Changing Mechanisms from Data, Learning Models of Individual Behavior in Chess, Minimizing Congestion for Balanced Dominators, Extracting Relevant Information from User's Utterances in Conversational Search and Recommendation, Nonlinearity Encoding for Extrapolation of Neural Networks, Learning Fair Representation via Distributional Contrastive Disentanglement, Predicting Opinion Dynamics via Sociologically-Informed Neural Networks, FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy, MetaV: A Meta-Verifier Approach to Task-Agnostic Model Fingerprinting, Compute Like Humans: Interpretable Step-by-step Symbolic Computation with Deep Neural Network, Bilateral Dependency Optimization: Defending Against Model-inversion Attacks, Evaluating Knowledge Graph Accuracy Powered by Optimized Human-machine Collaboration, Rep2Vec: Repository Embedding via Heterogeneous Graph Adversarial Contrastive Learning, External Knowledge Infusion for Tabular Pre-training Models with Dual-adapters, Releasing Private Data for Numerical Queries, Importance Prioritized Policy Distillation, Synthesising Audio Adversarial Examples for Automatic Speech Recognition, p-Meta: Towards On-device Deep Model Adaptation, Fair and Interpretable Models for Survival Analysis, Graph-Flashback Network for Next Location Recommendation, SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs, DICE: Domain-attack Invariant Causal Learning for Improved Data Privacy Protection and Adversarial Robustness, Semi-supervised Drifted Stream Learning with Short Lookback, Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking, A Generalized Backward Compatibility Metric, Balancing Bias and Variance for Active Weakly Supervised Learning, On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification, Active Model Adaptation Under Unknown Shift, Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting, Multi-View Clustering for Open Knowledge Base Canonicalization, Deep Learning for Prognosis Using Task-fMRI: A Novel Architecture and Training Scheme, Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation, State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation, Robust and Informative Text Augmentation (RITA) via Constrained Worst-Case Transformations for Low-Resource Named Entity Recognition, GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks, Learning on Graphs with Out-of-Distribution Nodes, RGVisNet: A Hybrid Retrieval-Generation Neural Framework Towards Automatic Data Visualization Generation, Towards an Optimal Asymmetric Graph Structure for Robust Semi-supervised Node Classification, ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data, Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems, Knowledge Enhanced Search Result Diversification, Causal Attention for Interpretable and Generalizable Graph Classification, Demystify Hyperparameters for Stochastic Optimization with Transferable Representations, GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks, pureGAM: Learning an Inherently Pure Additive Model, Learning Optimal Priors for Task-Invariant Representations in Variational Autoencoders, Clustering with Fair-Center Representation: Parameterized Approximation Algorithms and Heuristics, Incremental Cognitive Diagnosis for Intelligent Education, Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty, Aligning Dual Disentangled User Representations from Ratings and Textual Content, Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow, Towards Representation Alignment and Uniformity in Collaborative Filtering, Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction, A Model-Agnostic Approach to Differentially Private Topic Mining, Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation, Estimating Individualized Causal Effect with Confounded Instruments, Make Fairness More Fair: Fair Item Utility Estimation and Exposure Re-Distribution, Streaming Graph Neural Networks with Generative Replay, Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing, Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer, Task-Adaptive Few-shot Node Classification, Partial Label Learning with Discrimination Augmentation, Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning, Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage, Graph Neural Networks with Node-wise Architecture, Debiasing Learning for Membership Inference Attacks Against Recommender Systems, Invariant Preference Learning for General Debiasing in Recommendation, An Embedded Feature Selection Framework for Control, Comprehensive Fair Meta-learned Recommender System, SagDRE: Sequence-Aware Graph-Based Document-Level Relation Extraction with Adaptive Margin Loss, Disentangled Dynamic Heterogeneous Graph Learning for Opioid Overdose Prediction, Beyond Point Prediction: Capturing Zero-Inflated & Heavy-Tailed Spatiotemporal Data with Deep Extreme Mixture Models, Multi-fidelity Hierarchical Neural Processes, Domain Adaptation with Dynamic Open-Set Targets, Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction, CLARE: A Semi-supervised Community Detection Algorithm, Geometric Policy Iteration for Markov Decision Processes, Robust Tensor Graph Convolutional Networks via T-SVD based Graph Augmentation, Self-Supervised Hypergraph Transformer for Recommender Systems, Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data, RetroGraph: Retrosynthetic Planning with Graph Search, Ultrahyperbolic Knowledge Graph Embeddings, End-to-End Semi-Supervised Ordinal Regression AUC Maximization with Convolutional Kernel Networks, MetaPTP: An Adaptive Meta-optimized Model for Personalized Spatial Trajectory Prediction, Towards a Native Quantum Paradigm for Graph Representation Learning: A Sampling-based Recurrent Embedding Approach, Solving the Batch Stochastic Bin Packing Problem in Cloud: A Chance-constrained Optimization Approach, On-Device Learning for Model Personalization with Large-Scale Cloud-Coordinated Domain Adaption, Enhancing Machine Learning Approaches for Graph Optimization Problems with Diversifying Graph Augmentation, HICF: Hyperbolic Informative Collaborative Filtering, Toward Real-life Dialogue State Tracking Involving Negative Feedback Utterances, Numerical Tuple Extraction from Tables with Pre-training, Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions, Reinforcement Subgraph Reasoning for Fake News Detection, Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation, TrajGAT: A Graph-based Long-term Dependency Modeling Approach for Trajectory Similarity Computation, Learning Classifiers under Delayed Feedback with a Time Window Assumption, Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting, LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization, Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes, Nimble GNN Embedding with Tensor-Train Decomposition, Accurate Node Feature Estimation with Structured Variational Graph Autoencoder, Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream, ROLAND: Graph Learning Framework for Dynamic Graphs, Multiplex Heterogeneous Graph Convolutional Network, MDP2 Forest: A Constrained Continuous Multi-dimensional Policy Optimization Approach for Short-video Recommendation, Intrinsic-Motivated Sensor Management: Exploring with Physical Surprise, Dual Bidirectional Graph Convolutional Networks for Zero-shot Node Classification, M3Care: Learning with Missing Modalities in Multimodal Healthcare Data, Physics-infused Machine Learning for Crowd Simulation, Few-shot Heterogeneous Graph Learning via Cross-domain Knowledge Transfer, M-Mix: Generating Hard Negatives via Multi-sample Mixing for Contrastive Learning, Multi-Agent Graph Convolutional Reinforcement Learning for Dynamic Electric Vehicle Charging Pricing, MetroGAN: Simulating Urban Morphology with Generative Adversarial Network, Model Degradation Hinders Deep Graph Neural Networks, Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification, Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning, Unsupervised Key Event Detection from Massive Text Corpora, FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients, Adaptive Learning for Weakly Labeled Streams, Adaptive Fairness-Aware Online Meta-Learning for Changing Environments, MT-FlowFormer: A Semi-Supervised Flow Transformer for Encrypted Traffic Classification, Contrastive Learning with Complex Heterogeneity, Instant Graph Neural Networks for Dynamic Graphs, KRATOS: Context-Aware Cell Type Classification and Interpretation using Joint Dimensionality Reduction and Clustering, Unified 2D and 3D Pre-Training of Molecular Representations, How does Heterophily Impact the Robustness of Graph Neural Networks? More and more communities from both academia and industry have initiated the endeavors to solve these challenges. lhamdulillah! The categories include a basic Machine Learning model, model from learning dataset, CNN with real-world image dataset, NLP Text Classification with real-world text dataset, and Sequence Model with the real-world numeric dataset. Buy and download now. This international workshop on "Deep Learning on Graphs: Method and Applications (DLG-KDD'22)" aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to above challenges. Built with modern web technologies, our tool runs locally in users' web browsers or computational notebooks, lowering the barrier to use. Most of the code is adopted from AutoGluon (https://auto.gluon.ai/), a recent open-source AutoML toolkit that is both state-of-the-art and easy-to-use. The TensorFlow certification training is conducted through live streaming. You will have three attempts to pass and get the tensorflow developer certificate. This indicates overfitting, and the last epochs model weights are not the best one for sure. The two training modes are jointly performed as a multi-objective learning process, such that the ads of high relevance and CTR can be favored by the generated embeddings. While defining good long-term user experience is still an active research area, we focus on one specific aspect of improved long-term user experience here, which is user revisiting the platform. It is always a better idea to use the best model for inference or testing on images and videos after training. The workshop program consists of keynotes, invited talks, accepted technical paper presentations, as well as a digital agriculture hackathon panel. --color-bg-linkset-items:#797979; To realize this goal, we propose ReprBERT, which has the advantages of both excellent performance and low latency, by distilling the interaction-based BERT model to a representation-based architecture. Zakat ul Fitr (Fitrana) Fidya. The function to test the models by iterating over the test loader is very similar to the validation function that we used during the training. Properties of these nodes and edges directly map to business problems in the financial world. Moreover, CWTM has been deployed on the training platform of Alibaba advertising systems and achieved substantial improvements of ROI and CVR by 16.8% and 9.6%, respectively.
AI & Machine Learning Research Papers I have a convolutional Autoencoder (so its not a very big network), but I have a very big dataset: For one epoch, I have ` 16 (batch_size) * 7993 = 12788. images, each images dimension is 51 x 51 x 51. Select the desired language and hit Download.. PuTTY download is available on Windows, Linux, and Unix-like operating systems. defeated the Dota 2 world champions in a best-of-three match (20); won 99.4% of over 7000 games during a multi-day online showcase. From the same working directory, execute the following command in the terminal. The large size of object detection models deters their deployment in real-world applications such as self-driving cars and robotics. Still, lets go over some of the important aspects of it. META consists of Positional Encoding, Transformer-based Autoencoder, and Multi-task Prediction to learn effective representations for both migration prediction and rating prediction. With Flexi-pass, Simplilearn gives you access to all classes for 90 days so that you have the flexibility to choose sessions as per your convenience. They introduce Vision Transformer (ViT), which is applied directly to sequences of image patches by analogy with tokens (words) in NLP. The paper received an Honorable Mention at ICML 2020. Typically, it requires complex mathematical logic and background knowledge for solving mathematical problems. Its core idea is using globally hardest samples to subvert model training. .fb_hidden{position:absolute;top:-10000px;z-index:10001}.fb_reposition{overflow:hidden;position:relative}.fb_invisible{display:none}.fb_reset{background:none;border:0;border-spacing:0;color:#000;cursor:auto;direction:ltr;font-family:'lucida grande', tahoma, verdana, arial, sans-serif;font-size:11px;font-style:normal;font-variant:normal;font-weight:normal;letter-spacing:normal;line-height:1;margin:0;overflow:visible;padding:0;text-align:left;text-decoration:none;text-indent:0;text-shadow:none;text-transform:none;visibility:visible;white-space:normal;word-spacing:normal}.fb_reset>div{overflow:hidden}@keyframes fb_transform{from{opacity:0;transform:scale(.95)}to{opacity:1;transform:scale(1)}}.fb_animate{animation:fb_transform .3s forwards}
machine-perception-robotics-group/MPRGDeepLearningLectureNotebook In other use cases, anomaly detection is solved as a classification problem (either as node, edge classification in graph, row-based classification in tabular data or sequential classification in sequence data). We offer 24/7 support through email, chat, and calls. During the exam, there will be five categories and students will complete five models, one from each category. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We investigate the effectiveness of different standard machine learning algorithms and conclude these models deliver inferior performance. 30-day money-back guarantee* Prices include VAT where applicable. The outputs folder contains the weights while saving the best and last epoch models in PyTorch during training. Offline experiments over two real-world industry-scale datasets under different P&D services (i.e., food delivery and package pick-up) and online A/B test demonstrate the superiority of our proposed model. This program is required to start. In this paper, we present TAG, a high-quality concept matching dataset consisting of 10,000 labeled pairs of fine-grained concepts and web-styled natural language sentences, mined from open-domain social media content. One promising direction is to use known phenotype concepts to guide topic inference. The Fragile EarthWorkshop is a recurring event that gathers the research community to find and explore howdata science can measure and progress climate and social issues, following the framework of the United Nations Sustainable Development Goals (SDGs). The research group from the University of Oxford studies the problem of learning 3D deformable object categories from single-view RGB images without additional supervision. .Page-footer {margin-top: 24px;} Increasing corpus further will allow it to generate a more credible pastiche but not fix its fundamental lack of comprehension of the world. Extensive experiments on two real-world datasets show that our proposed framework achieves superior performance over state-of-the-art baselines in terms of improving the data fidelity and data utility in facilitating practical applications. With AI based solutions in high-stakes domains such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. Estimating user intent in real time is the only feasible way to personalize. We need one training set, one validation set, and one test set as well. In addition, GPS stations and seismometers may be deployed in large numbers across different locations and may produce a significant volume of data, consequently affecting the response time and the robustness of EEW systems. We conduct extensive experiments in three metropolis with real-world data and prove that our method outperforms the best baseline, reducing 9.01% infections and 12.27% deaths.We further demonstrate the explainability of the RL model, adding to its credibility and also enlightening the experts in turn. This is an implementation of the VQ-VAE (Vector Quantized Variational Autoencoder) and Convolutional Varational Autoencoder. In this paper, we consider the corporate credit rating migration early prediction problem, which predicts the credit rating of an issuer will be upgraded, unchanged, or downgraded after 12 months based on its latest financial reporting information at the time. .GoogleDfpAd { It would not be feasible to pre-compute recommended actions for all personalization scenarios beyond a certain scale. The non-stationary user traffic and bid landscape further worsen the situation, making the assignment unsupervised and hard to evaluate. This completes the dataset preparation part as well. As such, we developed an interdisciplinary Program Committee with significant experience in various aspects of AI, cybersecurity, and/or deployable defense. First, they suggest decomposing the posterior as the sum of a prior and an update. --color-bg-linkset-items-center:#696969; The only difference here from the validation function is that we are not calculating the loss. Although deep neural networks (DNNs) have been successfully deployed in various real-world application scenarios, recent studies demonstrated that DNNs are extremely vulnerable to adversarial attacks. In this paper, we present a novel representation learning framework Uni-Retriever developed for Bing Search, which unifies two different training modes knowledge distillation and contrastive learning to realize both required objectives. We design a self-supervised contact network representation algorithm to fuse the heterogeneous information for efficient vaccine allocation decision making. The paper received the Best Paper Award at ACL 2020, the leading conference in natural language processing. Neuroleptic malignant syndrome (NMS) is a rare, but life-threatening, idiosyncratic reaction to neuroleptic medications that is characterized by fever, muscular rigidity, altered mental status, and autonomic dysfunction.NMS often occurs shortly after the initiation of. We explicitly consider and alleviate the negative impact of uncertainty caused by network jitter and congestion, which are pervasive in complicated network environments. Applying introduced methods to other zero-sum two-team continuous environments. Adam) or accelerated schemes (e.g. Just the accuracy which we are returning at the end as well. This enables intelligent decision making and information transferring on the devices and unleashes the power of AIoT (Artificial Intelligence of Things) that supports applications such as smart city/agriculture/manufacturing/health care and self-driving scenarios. The source code is in https://github.com/lqfarmer/CCDR. Data science draws from methodology developed in such fields as applied mathematics, statistics, machine learning, data mining, data management, visualization, and HCI. DDGP is a two pronged algorithm that consists of cluster merging, followed by cluster boundary refinement.
Pytorch Select the desired language and hit Download.. PuTTY download is available on Windows, Linux, and Unix-like operating systems. Subscribe to our AI Research mailing list at the bottom of this article, A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning, Efficiently Sampling Functions from Gaussian Process Posteriors, Dota 2 with Large Scale Deep Reinforcement Learning, Beyond Accuracy: Behavioral Testing of NLP models with CheckList, EfficientDet: Scalable and Efficient Object Detection, Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild, An Image is Worth 1616 Words: Transformers for Image Recognition at Scale, AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients, Elliot Turner, CEO and founder of Hyperia, Graham Neubig, Associate professor at Carnegie Mellon University, they are still evaluating the risks and benefits, Gary Marcus, CEO and founder of Robust.ai, https://github.com/google/automl/tree/master/efficientdet, https://github.com/juntang-zhuang/Adabelief-Optimizer, GPT-3 & Beyond: 10 NLP Research Papers You Should Read, Novel Computer Vision Research Papers From 2020, AAAI 2021: Top Research Papers With Business Applications, 10 Leading Language Models For NLP In 2022, NeurIPS 2021 10 Papers You Shouldnt Miss, Why Graph Theory Is Cooler Than You Thought, Pretrain Transformers Models in PyTorch Using Hugging Face Transformers.
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