24 citations · 81 across the 9 of their papers we have counts for
9 papers
GPTVQ: The Blessing of Dimensionality for LLM Quantization
Mart van Baalen, Andrey Kuzmin, Ivan Koryakovskiy +6
In this work we show that the size versus accuracy trade-off of neural network quantization can be significantly improved by increasing the quantization dimensionality. We propose…
Pruning vs Quantization: Which is Better?
Andrey Kuzmin, Markus Nagel, Mart van Baalen +2
Neural network pruning and quantization techniques are almost as old as neural networks themselves. However, to date only ad-hoc comparisons between the two have been published. In…
FP8 versus INT8 for efficient deep learning inference
Mart van Baalen, Andrey Kuzmin, Suparna S Nair +8
Recently, the idea of using FP8 as a number format for neural network training has been floating around the deep learning world. Given that most training is currently conducted wit…
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…
Quantized Sparse Weight Decomposition for Neural Network Compression
Andrey Kuzmin, Mart van Baalen, Markus Nagel +1
In this paper, we introduce a novel method of neural network weight compression. In our method, we store weight tensors as sparse, quantized matrix factors, whose product is comput…
Cyclical Pruning for Sparse Neural Networks
Suraj Srinivas, Andrey Kuzmin, Markus Nagel +3
Current methods for pruning neural network weights iteratively apply magnitude-based pruning on the model weights and re-train the resulting model to recover lost accuracy. In this…
Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks
Andrey Kuzmin, Markus Nagel, Saurabh Pitre +3
The success of deep neural networks in many real-world applications is leading to new challenges in building more efficient architectures. One effective way of making networks more…
Set2Model Networks: Learning Discriminatively To Learn Generative Models
A. Vakhitov, A. Kuzmin, V. Lempitsky
We present a new "learning-to-learn"-type approach that enables rapid learning of concepts from small-to-medium sized training sets and is primarily designed for web-initialized im…
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo
Andrey Kuzmin, Dmitry Mikushin, Victor Lempitsky
We present a new deep learning-based approach for dense stereo matching. Compared to previous works, our approach does not use deep learning of pixel appearance descriptors, employ…