no code implementations • 14 Oct 2024 • Etai Littwin, Vimal Thilak, Anand Gopalakrishnan
We condition the target encoder and context encoder modules in IJEPA with positions of context and target windows respectively.
1 code implementation • 27 May 2024 • Anand Gopalakrishnan, Aleksandar Stanić, Jürgen Schmidhuber, Michael Curtis Mozer
Current state-of-the-art synchrony-based models encode object bindings with complex-valued activations and compute with real-valued weights in feedforward architectures.
1 code implementation • 13 Nov 2023 • Joonsu Gha, Vincent Herrmann, Benjamin Grewe, Jürgen Schmidhuber, Anand Gopalakrishnan
Our novel MusicSlots method adapts SlotAttention to the audio domain, to achieve unsupervised music decomposition.
1 code implementation • 30 May 2023 • Kazuki Irie, Anand Gopalakrishnan, Jürgen Schmidhuber
To scale to such challenging tasks, we focus on certain well-known neural architectures with element-wise recurrence, allowing for tractable RTRL without approximation.
no code implementations • 26 May 2023 • Mingchen Zhuge, Haozhe Liu, Francesco Faccio, Dylan R. Ashley, Róbert Csordás, Anand Gopalakrishnan, Abdullah Hamdi, Hasan Abed Al Kader Hammoud, Vincent Herrmann, Kazuki Irie, Louis Kirsch, Bing Li, Guohao Li, Shuming Liu, Jinjie Mai, Piotr Piękos, Aditya Ramesh, Imanol Schlag, Weimin Shi, Aleksandar Stanić, Wenyi Wang, Yuhui Wang, Mengmeng Xu, Deng-Ping Fan, Bernard Ghanem, Jürgen Schmidhuber
What should be the social structure of an NLSOM?
1 code implementation • NeurIPS 2023 • Aleksandar Stanić, Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber
Current state-of-the-art object-centric models use slots and attention-based routing for binding.
1 code implementation • 25 Mar 2022 • Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Sjoerd van Steenkiste
The discovery of reusable sub-routines simplifies decision-making and planning in complex reinforcement learning problems.
1 code implementation • ICLR 2021 • Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber
We propose PermaKey, a novel approach to representation learning based on object keypoints.
1 code implementation • CVPR 2019 • Anand Gopalakrishnan, Ankur Mali, Dan Kifer, C. Lee Giles, Alexander G. Ororbia
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 less computation.