Research Hub
Papers with a full citation graph - what each paper draws on, and who cites it.
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)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)
2 papers
2012Advances in Neural Information Processing Systems (NeurIPS) 2012, pp. 1097-1105
ImageNet Classification with Deep Convolutional Neural Networks
Finding: AlexNet won the ImageNet 2012 competition by a large margin (15.3% top-5 error vs 26.2% for the second best), proving that deep CNNs are highly effective on large-scale data.
CNNImageNet
2001Machine Learning, vol. 45, no. 1, pp. 5-32, DOI: 10.1023/A:1010933404324
Random Forests
Finding: Random Forest was shown to outperform single trees and other ensemble methods, handling high-dimensional and noisy data well.
Random Forestdecision tree