Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Gopalakrishnan, Anand"'
Image-based Joint-Embedding Predictive Architecture (IJEPA) offers an attractive alternative to Masked Autoencoder (MAE) for representation learning using the Masked Image Modeling framework. IJEPA drives representations to capture useful semantic in
Externí odkaz:
http://arxiv.org/abs/2410.10773
Current state-of-the-art synchrony-based models encode object bindings with complex-valued activations and compute with real-valued weights in feedforward architectures. We argue for the computational advantages of a recurrent architecture with compl
Externí odkaz:
http://arxiv.org/abs/2405.17283
Current object-centric learning models such as the popular SlotAttention architecture allow for unsupervised visual scene decomposition. Our novel MusicSlots method adapts SlotAttention to the audio domain, to achieve unsupervised music decomposition
Externí odkaz:
http://arxiv.org/abs/2311.07534
Real-time recurrent learning (RTRL) for sequence-processing recurrent neural networks (RNNs) offers certain conceptual advantages over backpropagation through time (BPTT). RTRL requires neither caching past activations nor truncating context, and ena
Externí odkaz:
http://arxiv.org/abs/2305.19044
Autor:
Zhuge, Mingchen, Liu, Haozhe, Faccio, Francesco, Ashley, Dylan R., Csordás, Róbert, Gopalakrishnan, Anand, Hamdi, Abdullah, Hammoud, Hasan Abed Al Kader, Herrmann, Vincent, Irie, Kazuki, Kirsch, Louis, Li, Bing, Li, Guohao, Liu, Shuming, Mai, Jinjie, Piękos, Piotr, Ramesh, Aditya, Schlag, Imanol, Shi, Weimin, Stanić, Aleksandar, Wang, Wenyi, Wang, Yuhui, Xu, Mengmeng, Fan, Deng-Ping, Ghanem, Bernard, Schmidhuber, Jürgen
Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each other in a "mindstorm." Recent implementations of NN-based societies of
Externí odkaz:
http://arxiv.org/abs/2305.17066
Current state-of-the-art object-centric models use slots and attention-based routing for binding. However, this class of models has several conceptual limitations: the number of slots is hardwired; all slots have equal capacity; training has high com
Externí odkaz:
http://arxiv.org/abs/2305.15001
The discovery of reusable sub-routines simplifies decision-making and planning in complex reinforcement learning problems. Previous approaches propose to learn such temporal abstractions in a purely unsupervised fashion through observing state-action
Externí odkaz:
http://arxiv.org/abs/2203.13573
We propose PermaKey, a novel approach to representation learning based on object keypoints. It leverages the predictability of local image regions from spatial neighborhoods to identify salient regions that correspond to object parts, which are then
Externí odkaz:
http://arxiv.org/abs/2011.12930
Publikováno v:
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12116-12125. 2019
We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive with prior work in short-term prediction and requiring significantly le
Externí odkaz:
http://arxiv.org/abs/1809.03036
Autor:
Gopalakrishnan, Anand1,2,3 (AUTHOR) anand@idsia.ch, Irie, Kazuki1,2,3 (AUTHOR) kazuki@idsia.ch, Schmidhuber, Jürgen1,2,3,4 (AUTHOR) juergen@idsia.ch, van Steenkiste, Sjoerd5 (AUTHOR) svansteenkiste@google.com
Publikováno v:
Neural Computation. Apr2023, Vol. 35 Issue 4, p593-626. 34p. 2 Diagrams, 19 Charts, 7 Graphs.
