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Found 8 papers, 2 papers with code
Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google
To tackle these challenges, we propose a shared input generation technique to reduce computational load of input generation by amortizing costs across many models.
FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models
To address the limited data resources and reduce the annotation cost, we introduce FinLLMs, a method for generating financial question-answering data based on common financial formulas using Large Language Models.
SuPerPM: A Large Deformation-Robust Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data
Thus, for tuning the learning model, we gather endoscopic data of soft tissue being manipulated by a surgical robot and then establish correspondences between point clouds at different time points to serve as ground truth.
AnyOKP: One-Shot and Instance-Aware Object Keypoint Extraction with Pretrained ViT
Towards flexible object-centric visual perception, we propose a one-shot instance-aware object keypoint (OKP) extraction approach, AnyOKP, which leverages the powerful representation ability of pretrained vision transformer (ViT), and can obtain keypoints on multiple object instances of arbitrary category after learning from a support image.
Symbolic Discovery of Optimization Algorithms
On diffusion models, Lion outperforms Adam by achieving a better FID score and reducing the training compute by up to 2. 3x.
A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)
However, MCML metrics based on model counting show that the performance can degrade substantially when tested against the entire (bounded) input space, indicating the high complexity of precisely learning these properties, and the usefulness of model counting in quantifying the true performance.
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees
We propose MoET, a more expressive, yet still interpretable model based on Mixture of Experts, consisting of a gating function that partitions the state space, and multiple decision tree experts that specialize on different partitions.
MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
By training Mo\"ET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models.
