Deep Learning Research at ICML

Two Sigma is actively engaged in advancing deep learning research, with a focus on improving recurrent neural networks (RNNs) through techniques like unitary evolution and orthogonal weight matrices to address vanishing/exploding gradients. The company is also exploring unsupervised learning objectives to augment supervised training for large-scale image classification and deconstructing architectures like Ladder Networks. Furthermore, research is ongoing in supervised training methods, including dropout distillation and normalization propagation for better stability and performance, as well as gradient-based tuning of regularization hyperparameters. Deep reinforcement learning is also an area of focus, leveraging deep learning for complex tasks.