most citedQ-S5: Towards Quantized State Space Models

2 citations · 2 across the 2 of their papers we have counts for

collaborators

6 papers

cs.NE2026

Encoding and Decoding Temporal Signals with Spiking Bandpass Wavelets

Jens Egholm Pedersen, Tony Lindeberg, Peter Gerstoft

Spike-based encodings are sparse and energy-efficient, but have largely been formulated probabilistically, disconnected from most signal processing literature. We recast spike enco…

cs.NE2026

Scale-covariant spiking wavelets

Jens Egholm Pedersen, Tony Lindeberg, Peter Gerstoft

We establish a theoretical connection between wavelet transforms and spiking neural networks through scale-space theory. We rely on the scale-covariant guarantees in the leaky inte…

cs.CV2024

GERD: Geometric event response data generation

Jens Egholm Pedersen, Dimitris Korakovounis, Jörg Conradt

Event-based vision sensors offer high temporal resolution, high dynamic range, and low power consumption, yet event-based vision models lag behind conventional frame-based vision m…

cs.NE2024

Neuromorphic Programming: Emerging Directions for Brain-Inspired Hardware

Steven Abreu, Jens E. Pedersen

The value of brain-inspired neuromorphic computers critically depends on our ability to program them for relevant tasks. Currently, neuromorphic hardware often relies on machine le…

cs.LG20242 cited

Q-S5: Towards Quantized State Space Models

Steven Abreu, Jens E. Pedersen, Kade M. Heckel +1

In the quest for next-generation sequence modeling architectures, State Space Models (SSMs) have emerged as a potent alternative to transformers, particularly for their computation…

cs.NE2024

Covariant spatio-temporal receptive fields for spiking neural networks

Jens Egholm Pedersen, Jörg Conradt, Tony Lindeberg

Biological nervous systems constitute important sources of inspiration towards computers that are faster, cheaper, and more energy efficient. Neuromorphic disciplines view the brai…