Statistical Learning
Learning a latent manifold of odor representations from neural responses in piriform cortex
Anqi Wu, Stan Pashkovski, Sandeep R. Datta, Jonathan W. Pillow
Neural Information Processing Systems http://nips.cc/
Stein Variational Gradient Descentwith Matrix-Valued Kernels
See Liu & Wang (2016, 2018); Liu (2017) formoredetails.
Supplementary Material for Learning Energy-based Model via Dual-MCMC Teaching
We show additional image synthesis in Fig.2. For reported numbers in main text, we adopt the network structure that contains Residue Blocks (see implementation details in Tab.5). We then test our model for the task of image inpainting. As shown in Fig.1, our This is the marginal version of Eqn.8 shown in the main text. 2 2.3 Learning Algorithm Three models are trained in an alternative and iterative manner based on the current model parameters. Compared to Eqn.3 and Eqn.6 in the main text, Eqn.5 and Eqn.6 start with initial points initialized We present the learning algorithm in Alg.1.
Learning Energy-based Model via Dual-MCMC Teaching
This paper studies the fundamental learning problem of the energy-based model (EBM).
Certainty Equivalence is Efficient for Linear Quadratic Control
Horia Mania, Stephen Tu, Benjamin Recht
Neural Information Processing Systems http://nips.cc/
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Abhishek Kumar, Rogerio Feris, Raja Giryes, Alex Bronstein
Neural Information Processing Systems http://nips.cc/
Adversarial Training and Robustness for Multiple Perturbations
Neural Information Processing Systems http://nips.cc/
Y our representations are in the network: composable and parallel adaptation for large scale models
On the ViT -L/16 architecture, our experiments show that a single adapter, 1.3% of the full model, is able to reach full fine-tuning accuracy on average across 11 challenging downstream classification tasks. Compared with other forms of parameter-efficient adaptation, the isolated nature of the InCA adaptation is computationally desirable for large-scale models. For instance, we adapt ViT -G/14 (1.8B+ parameters) quickly with 20+ adapters in parallel on a single V100 GPU (76% GPU memory reduction) and exhaustively identify its