Search Results for author:
Found 9 papers, 3 papers with code
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
Existing causal discovery methods based on combinatorial optimization or search are slow, prohibiting their application on large-scale datasets.
Understanding prompt engineering may not require rethinking generalization
Zero-shot learning in prompted vision-language models, the practice of crafting prompts to build classifiers without an explicit training process, has achieved impressive performance in many settings.
Partial Identifiability for Domain Adaptation
In general, without further assumptions, the joint distribution of the features and the label is not identifiable in the target domain.
Pattern Detection in the Activation Space for Identifying Synthesized Content
Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise.
Towards creativity characterization of generative models via group-based subset scanning
We propose group-based subset scanning to quantify, detect, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of generative models.
Identifying Audio Adversarial Examples via Anomalous Pattern Detection
Audio processing models based on deep neural networks are susceptible to adversarial attacks even when the adversarial audio waveform is 99. 9% similar to a benign sample.
Deep Mining: Detecting Anomalous Patterns in Neural Network Activations with Subset Scanning
This work introduces ``Subset Scanning methods from the anomalous pattern detection domain to the task of detecting anomalous inputs to neural networks.
Characterizing the hyper-parameter space of LSTM language models for mixed context applications
Applying state of the art deep learning models to novel real world datasets gives a practical evaluation of the generalizability of these models.
