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Found 9 papers, 4 papers with code
Carbon Aware Transformers Through Joint Model-Hardware Optimization
The rapid growth of machine learning (ML) systems necessitates a more comprehensive evaluation of their environmental impact, particularly their carbon footprint, which comprises operational carbon from training and inference execution and embodied carbon from hardware manufacturing and its entire life-cycle.
Towards Understanding Camera Motions in Any Video
We introduce CameraBench, a large-scale dataset and benchmark designed to assess and improve camera motion understanding.
Aligned Multi Objective Optimization
To date, the multi-objective optimization literature has mainly focused on conflicting objectives, studying the Pareto front, or requiring users to balance tradeoffs.
On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
In this paper, we take a further step and introduce a \emph{personalized} FedRL framework (PFedRL) by taking advantage of possibly shared common structure among agents in heterogeneous environments.
NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples
Vision-language models (VLMs) have made significant progress in recent visual-question-answering (VQA) benchmarks that evaluate complex visio-linguistic reasoning.
Pearl: A Production-ready Reinforcement Learning Agent
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals.
On Noisy Evaluation in Federated Hyperparameter Tuning
In this work, we perform the first systematic study on the effect of noisy evaluation in federated hyperparameter tuning.
Interpretable Personalized Experimentation
Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments.
Extreme Multi-label Learning for Semantic Matching in Product Search
In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products.
Extreme Multi-Label Classification
MUlTI-LABEL-ClASSIFICATION
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