Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Van keirsbilck, Matthijs"'
Autor:
Dong, Xin, Fu, Yonggan, Diao, Shizhe, Byeon, Wonmin, Chen, Zijia, Mahabaleshwarkar, Ameya Sunil, Liu, Shih-Yang, Van Keirsbilck, Matthijs, Chen, Min-Hung, Suhara, Yoshi, Lin, Yingyan, Kautz, Jan, Molchanov, Pavlo
We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) for enhanced efficiency. Attention heads provide high-resolution recall,
Externí odkaz:
http://arxiv.org/abs/2411.13676
Autor:
Aoudia, Fayçal Aït, Hoydis, Jakob, Cammerer, Sebastian, Van Keirsbilck, Matthijs, Keller, Alexander
We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) and carrier frequency offset (CFO) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT). The in
Externí odkaz:
http://arxiv.org/abs/2205.10805
Publikováno v:
INTERSPEECH 2021
We demonstrate that 1x1-convolutions in 1D time-channel separable convolutions may be replaced by constant, sparse random ternary matrices with weights in $\{-1,0,+1\}$. Such layers do not perform any multiplications and do not require training. More
Externí odkaz:
http://arxiv.org/abs/2103.17142
Artificial neural networks can be represented by paths. Generated as random walks on a dense network graph, we find that the resulting sparse networks allow for deterministic initialization and even weights with fixed sign. Such networks can be train
Externí odkaz:
http://arxiv.org/abs/2103.03543
Recurrent neural networks (RNNs) are omnipresent in sequence modeling tasks. Practical models usually consist of several layers of hundreds or thousands of neurons which are fully connected. This places a heavy computational and memory burden on hard
Externí odkaz:
http://arxiv.org/abs/1905.12340
Low bit-width integer weights and activations are very important for efficient inference, especially with respect to lower power consumption. We propose Monte Carlo methods to quantize the weights and activations of pre-trained neural networks withou
Externí odkaz:
http://arxiv.org/abs/1905.12253
Today's Automatic Speech Recognition systems only rely on acoustic signals and often don't perform well under noisy conditions. Performing multi-modal speech recognition - processing acoustic speech signals and lip-reading video simultaneously - sign
Externí odkaz:
http://arxiv.org/abs/1803.04840
Publikováno v:
Journal of Supercomputing. Jul2023, Vol. 79 Issue 10, p11387-11409. 23p.
