43 citations · 77 across the 6 of their papers we have counts for
6 papers
QBitOpt: Fast and Accurate Bitwidth Reallocation during Training
Jorn Peters, Marios Fournarakis, Markus Nagel +2
Quantizing neural networks is one of the most effective methods for achieving efficient inference on mobile and embedded devices. In particular, mixed precision quantized (MPQ) net…
FP8 Quantization: The Power of the Exponent
Andrey Kuzmin, Mart Van Baalen, Yuwei Ren +3
When quantizing neural networks for efficient inference, low-bit integers are the go-to format for efficiency. However, low-bit floating point numbers have an extra degree of freed…
Self Normalizing Flows
T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini +3
Efficient gradient computation of the Jacobian determinant term is a core problem in many machine learning settings, and especially so in the normalizing flow framework. Most propo…
Integer Discrete Flows and Lossless Compression
Emiel Hoogeboom, Jorn W. T. Peters, Rianne van den Berg +1
Lossless compression methods shorten the expected representation size of data without loss of information, using a statistical model. Flow-based models are attractive in this setti…
Probabilistic Binary Neural Networks
Jorn W. T. Peters, Max Welling
Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks. In this work, we present a pr…
HexaConv
Emiel Hoogeboom, Jorn W. T. Peters, Taco S. Cohen +1
The effectiveness of Convolutional Neural Networks stems in large part from their ability to exploit the translation invariance that is inherent in many learning problems. Recently…