SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the
research and development of neural speech processing technologies by being simple …

Adapting large-scale pretrained language models to downstream tasks via fine-tuning is the
standard method for achieving state-of-the-art performance on NLP benchmarks. However …

Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining
highdimensional, low-precision, distributed representations of data. These representations …

In this work, we move beyond the traditional complex-valued representations, introducing
more expressive hypercomplex representations to model entities and relations for …

Abstract We introduce Clifford Group Equivariant Neural Networks: a novel approach for
constructing $\mathrm {O}(n) $-and $\mathrm {E}(n) $-equivariant models. We identify and …

Images are a convenient way for humans to obtain information and knowledge, but they are
often destroyed throughout the collection or distribution process. Therefore, image …

In this paper, we study the problem of learning representations of entities and relations in the
knowledge graph for the link prediction task. Our idea is based on the observation that the …

Partial differential equations (PDEs) see widespread use in sciences and engineering to
describe simulation of physical processes as scalar and vector fields interacting and …

Quaternion neural networks have recently received an increasing interest due to noticeable
improvements over real-valued neural networks on real world tasks such as image, speech …

Previous work on predicting or generating 3D human pose sequences regresses either joint
rotations or joint positions. The former strategy is prone to error accumulation along the …