Markus Kliegl
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View article: Llama-Nemotron: Efficient Reasoning Models
Llama-Nemotron: Efficient Reasoning Models Open
We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an open license for enterprise use. The family comes in three siz…
View article: Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset
Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset Open
Recent English Common Crawl datasets like FineWeb-Edu and DCLM achieved significant benchmark gains via aggressive model-based filtering, but at the cost of removing 90% of data. This limits their suitability for long token horizon trainin…
View article: Trace norm regularization and faster inference for embedded speech recognition RNNs
Trace norm regularization and faster inference for embedded speech recognition RNNs Open
We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR)…
View article: Reducing Bias in Production Speech Models
Reducing Bias in Production Speech Models Open
Replacing hand-engineered pipelines with end-to-end deep learning systems has enabled strong results in applications like speech and object recognition. However, the causality and latency constraints of production systems put end-to-end sp…
View article: Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting Open
Keyword spotting (KWS) constitutes a major component of human-technology interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate, while minimizing the footprint size, latency and complexity are the goals for KWS. Towar…