Deep Learning
Contrastive Active Inference
In contrast, reinforcement learning requires human-designed rewards to accomplish any desired outcome.
Supplementary Materials for " Private Set Generation with Discriminative Information "
Our privacy computation is based on the notion of Rรฉnyi-DP, which we recall as follows. Lastly, we use the following theorem to convert ( ฮฑ,ฮต) -RDP to (ฮต, ฮด) -DP . The total dataset size is 60K for the training set and 10K for the testing set, respectively. All our models and methods are implemented in PyTorch. Based on Google's TensorFlow privacy under version The experiments presented in Section 5.2 of the main paper correspond to the class-incremental learning setting [ And the task protocol is sequentially learning to classify a given sample into all the classes seen so far.
Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets-Supplementary Materials
In the input encoder layer, i.e. the 1st layer, all the tokens focus on themselves and the head tokens. And in the early stage, i.e. from the 2nd to 6th layers, all the tokens focus more on themselves and do
FP8 Quantization: The Power of the Exponent Andrey Kuzmin, Mart V an Baalen
Neural network quantization is one of the most effective ways to improve the efficiency of neural networks. Quantization allows weights and activations to be represented in low bit-width formats, e.g. 8 bit integers (INT8).
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation Jiaqi Gu
Optical computing is an emerging technology for next-generation efficient artificial intelligence (AI) due to its ultra-high speed and efficiency.
