🔥Best_Papers-AI_ML_CV_NLP_RO_SE
Do you love reading research papers?
If Yes, you have arrived at the right place. I spend a lot of time reading papers, researching techniques. It is a crucial part of my ML work. If you want to do research or you want to be a better ML engineer, then you should read papers. This habit of reading papers will help you to remain updated with the field.
|Artificial Intelligence | Machine Learning | Computer Vision | NLP | Robotics | Software Engineering|
List of files in the repo
Computer Vision
| Conference | Paper_Name | Year | Link |
|---|---|---|---|
| CVPR | BSP_Net- Generating Compact Meshes via Binary Space Partitioning | 2020 | BSP_Net |
| DeepCap-Monocular Human Performance Capture Using Weak Supervision | 2020 | Deep_Cap | |
| Unsupervised Learning of Probably Symmetric Deformable 3D Objects | 2020 | Unsupr_3d_obj | |
| ECCV | NeRF-Representing Scenes as Neural Radiance Fields for View Synthesis | 2020 | NeRF |
| RAFT- Recurrent All-Pairs Field Transforms for Optical Flow | 2020 | RAFT | |
| Towards Streaming Perception | 2020 | Streaming | |
| ICCV | Mask R-CNN | 2017 | MaskRCNN |
| DeepNeuralForest_poster | 2015 | DNF | |
| From Large Scale Image Categorization to Entry-Level Categories | 2013 | LSIC | |
| SinGAN-Learning a Generative Model From a Single Natural Image | 2019 | SinGAN |
Machine Learning
| Conference | Paper_Name | Year | Link |
|---|---|---|---|
| ICML | On Learning Sets of Symmetric Elements | 2020 | LSSE |
| Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems | 2020 | TPAIIP | |
| Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representation | 2019 | ULDR | |
| Rates of Convergence for Sparse Variational Gaussian Process Regression | 2019 | SVGPR | |
| Obfuscated Gradients Give a False Sense of Security- Circumventing Defenses to Adversarial Examples | 2018 | CDAE | |
| NIPS-NeurIPS | Language Models are Few-Shot Learners | 2020 | LMFSL |
| No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium | 2020 | LDEFCE |
NLP
| Conference | Paper_Name | Year | Link |
|---|---|---|---|
| ACL | Vocabulary Learning via Optimal Transport for Neural Machine Transalation | 2021 | VLOT-NMT |
| Beyond Accuracy-Behavioral Testing of NLP Models with CheckList | 2020 | BA-BTNLP | |
| Bridging the Gap between Training and Inference for Neural Machine Transalation | 2019 | BGTT-NMT | |
| SIGNLL | Acquiring language from speech by learning to remember and predict | 2020 | ALS-LRP |
SOFTWARE ENGINEERING
| Conference | Paper_Name | Year | Link |
|---|---|---|---|
| ACM | Deep Learning Library Testing via Effective Model Generation | 2020 | DLLT-EMG |
| On Decomposing a Deep Neural Network into Modules | 2020 | DNNM | |
| FSE | A principled approach to GraphQL query cost analysis | 2020 | GQL-CA |
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