13 citations · 43 across the 7 of their papers we have counts for
8 papers
Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap
Renbo Tu, Yang Liu, Shuying Sun
Generative retrieval (GR) has emerged as a strong candidate generator for sequential recommendation as language models and semantic-item identifiers have improved. However, GR is n…
Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
Renbo Tu, Colin White, Jean Kossaifi +5
Neural operators, such as Fourier Neural Operators (FNO), form a principled approach for learning solution operators for PDEs and other mappings between function spaces. However, m…
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations
Minghao Chen, Renbo Tu, Chenxi Huang +3
Previous work on action representation learning focused on global representations for short video clips. In contrast, many practical applications, such as video alignment, strongly…
AutoML for Climate Change: A Call to Action
Renbo Tu, Nicholas Roberts, Vishak Prasad +7
The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications. The climate c…
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies
Arjun Krishnakumar, Colin White, Arber Zela +3
Zero-cost proxies (ZC proxies) are a recent architecture performance prediction technique aiming to significantly speed up algorithms for neural architecture search (NAS). Recent w…
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
Renbo Tu, Nicholas Roberts, Mikhail Khodak +3
Most existing neural architecture search (NAS) benchmarks and algorithms prioritize well-studied tasks, e.g. image classification on CIFAR or ImageNet. This makes the performance o…
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Mikhail Khodak, Renbo Tu, Tian Li +4
Tuning hyperparameters is a crucial but arduous part of the machine learning pipeline. Hyperparameter optimization is even more challenging in federated learning, where models are…
Towards Deeper Generative Architectures for GANs using Dense connections
Samarth Tripathi, Renbo Tu
In this paper, we present the result of adopting skip connections and dense layers, previously used in image classification tasks, in the Fisher GAN implementation. We have experim…