Research Hub

Papers with a full citation graph - what each paper draws on, and who cites it.

Advanced search

All (51)robotics (6)deep learning (5)backpropagation (3)deep-learning (3)HRI (3)survey (3)algorithms (3)reasoning (2)actuators (2)machine-learning (2)

Show 177 more topics

optimization (2)LSTM (2)deep reinforcement learning (2)biped robot (2)representation-learning (2)force control (2)ensemble (2)regression (2)hypothesis-testing (2)statistics (2)autonomous agents (1)web environment (1)evaluation (1)UI automation (1)ReAct (1)function calling (1)agent (1)chain-of-thought (1)prompting (1)agents (1)sensors (1)review (1)automatic-differentiation (1)calculus (1)pose estimation (1)openpose (1)part affinity fields (1)computer vision (1)skeletal tracking (1)OCR (1)text recognition (1)CRNN (1)CTC (1)locomotion (1)Sim-to-Real (1)Transformer (1)attention (1)NLP (1)machine translation (1)object detection (1)YOLO (1)real-time vision (1)bounding boxes (1)gradient-descent (1)adam-optimizer (1)DQN (1)Q-learning (1)Atari games (1)transfer learning (1)feature extraction (1)CNNs (1)generalization (1)vector-spaces (1)word-embeddings (1)cosine-similarity (1)nlp (1)CNN (1)ImageNet (1)image classification (1)IMU (1)orientation filter (1)quaternion (1)ROS (1)Robot Operating System (1)open-source (1)middleware (1)service robot (1)elderly care (1)robot arm (1)imitation learning (1)learning from demonstration (1)recommender systems (1)matrix factorization (1)SVD (1)collaborative filtering (1)Netflix Prize (1)safety (1)collaborative robots (1)standards (1)robotic hand (1)pneumatic actuators (1)force sensor (1)design (1)ZMP (1)balance (1)control (1)human-robot interaction (1)social robots (1)service robotics (1)Random Forest (1)decision tree (1)classification (1)biped walking (1)inverted pendulum (1)gait pattern (1)humanoid robot (1)random forest (1)decision trees (1)supervised learning (1)motion planning (1)RRT (1)random tree (1)impedance (1)hybrid control (1)search engines (1)PageRank (1)eigenvectors (1)graph theory (1)Markov chains (1)localization (1)particle filter (1)navigation (1)mobile robot (1)mechanical design (1)ASIMO (1)humanoid (1)RNN (1)sequence modeling (1)vanishing gradient (1)visual servoing (1)image-based (1)position-based (1)lasso (1)regularization (1)feature selection (1)cross-validation (1)model evaluation (1)bootstrap (1)model selection (1)reinforcement learning (1)temporal difference (1)prediction (1)TD (1)edge detection (1)image processing (1)Canny filter (1)hysteresis (1)neural networks (1)representations (1)neural-networks (1)credit-assignment (1)convex-optimization (1)linear-programming (1)interior-point-methods (1)computational-complexity (1)least squares (1)history of statistics (1)Gauss (1)matrix multiplication (1)computational complexity (1)numerical linear algebra (1)K-Means (1)clustering (1)unsupervised learning (1)data analysis (1)logistic regression (1)binary data (1)maximum likelihood (1)kinematics (1)Denavit-Hartenberg (1)transformation matrix (1)markov-chain (1)monte-carlo (1)mcmc (1)statistical-mechanics (1)PID (1)Ziegler-Nichols (1)tuning (1)industrial control (1)statistical-significance (1)neyman-pearson (1)decision-theory (1)t-distribution (1)sample-size (1)bayes-theorem (1)probability (1)history-of-mathematics (1)

3 papers

2017Journal of Machine Learning Research (JMLR), Vol. 18, pp. 1-43 — https://jmlr.org/papers/v18/17-468.html

Automatic Differentiation in Machine Learning: a Survey

Alexey Andreyevich Radul, Atılım Güneş Baydin, Jeffrey Mark Siskind, Barak A. Pearlmutter

Finding: Reverse-mode AD is the foundational algorithm that powers the training of virtually all deep learning models — commonly known in the ML community as backpropagation.

automatic-differentiation backpropagation

2015International Conference on Learning Representations (ICLR 2015) — arXiv:1412.6980, https://arxiv.org/abs/1412.6980

Adam: A Method for Stochastic Optimization

Jimmy Ba, Diederik P. Kingma

Finding: Adam combines the advantages of AdaGrad (handles sparse gradients) and RMSProp (works well in non-stationary settings), and performs well empirically across problems.

optimization gradient-descent

1984Combinatorica, Vol. 4, No. 4, pp. 373-395 — https://doi.org/10.1007/BF02579150

A new polynomial-time algorithm for linear programming

Narendra Karmarkar

Finding: Proved the algorithm runs in polynomial time, answering the open question, and claimed practical speed advantages over Simplex for large problems.

convex-optimization linear-programming