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Found 10 papers, 7 papers with code
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners
Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously.
On the Reliability of Watermarks for Large Language Models
We also consider a range of new detection schemes that are sensitive to short spans of watermarked text embedded inside a large document, and we compare the robustness of watermarking to other kinds of detectors.
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix.
Ranked #12 on
Node Classification
on Reddit
A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs
We observe that in most cases, we need both a suitable domain generalization algorithm and a strong GNN backbone model to optimize out-of-distribution test performance.
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy
Randomized smoothing (RS) is an effective and scalable technique for constructing neural network classifiers that are certifiably robust to adversarial perturbations.
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
Innovations in neural architectures have fostered significant breakthroughs in language modeling and computer vision.
Ranked #137 on
Image Classification
on CIFAR-10
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results.
Robust Optimization as Data Augmentation for Large-scale Graphs
Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks).
Ranked #1 on
Graph Property Prediction
on ogbg-ppa
Data Augmentation for Meta-Learning
Conventional image classifiers are trained by randomly sampling mini-batches of images.
Good Semi-supervised VAE Requires Tighter Evidence Lower Bound
(2) Good semi-supervised learning results and good generative performance can not be obtained at the same time.
