1 citations · 2 across the 14 of their papers we have counts for
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Conditional Flow Models Convert Epistemic Uncertainty into Apparent Aleatoric Variance
Navodita Sharma, Abhishek Shetty, Max Simchowitz +1
Conditional Flow Models (CFMs) are increasingly being used in place of deterministic predictors across machine learning, including for tasks that were classically formulated as reg…
H2LooP Telecom Model v1: From Telecom Comprehension to Autonomous Issue and PR Resolution
Amit Singh, Vedant Nipane, Mayank Goel +2
We present H2LooP Telecom Model v1, a domain-specialized large language models fine-tuned for the telecommunications industry. We release two domain-adapted model variants serving…
Dynamic Compression in Recurrent Networks
Jyothish Pari, Ryan Bahlous-Boldi, Pulkit Agrawal
Recurrent models process long contexts efficiently by compressing their history into a fixed-size state, but modern architectures typically do so in a single causal pass over the s…
Fine-Tuning and Serving Gemma 4 31B on Google Cloud TPU: A Technical Comparison with GPU Baselines
Jatin Kishnani, Mayank Goel, Amit Singh +2
We present the first end-to-end demonstration of fine-tuning and serving Google's Gemma 4 31B model on TPU hardware, providing an empirical comparison of TPU and GPU platforms for…
H2LooP Spark Preview: Continual Pretraining of Large Language Models for Low-Level Embedded Systems Code
Amit Singh, Vedant Nipane, Pulkit Agrawal +2
Large language models (LLMs) demonstrate strong code generation abilities in general-purpose programming languages but remain limited in specialized domains such as low-level embed…
Training Language Models via Neural Cellular Automata
Dan Lee, Seungwook Han, Akarsh Kumar +1
Pre-training is crucial for large language models (LLMs), as it is when most representations and capabilities are acquired. However, natural language pre-training has problems: hig…
Self-Distillation Enables Continual Learning
Idan Shenfeld, Mehul Damani, Jonas Hübotter +1
Continual learning, enabling models to acquire new skills and knowledge without degrading existing capabilities, remains a fundamental challenge for foundation models. While on-pol…