4 research outputs found
Interactive Concept Bottleneck Models
Concept bottleneck models (CBMs) (Koh et al. 2020) are interpretable neural
networks that first predict labels for human-interpretable concepts relevant to
the prediction task, and then predict the final label based on the concept
label predictions.We extend CBMs to interactive prediction settings where the
model can query a human collaborator for the label to some concepts. We develop
an interaction policy that, at prediction time, chooses which concepts to
request a label for so as to maximally improve the final prediction. We
demonstrate thata simple policy combining concept prediction uncertainty and
influence of the concept on the final prediction achieves strong performance
and outperforms a static approach proposed in Koh et al. (2020) as well as
active feature acquisition methods proposed in the literature. We show that the
interactiveCBM can achieve accuracy gains of 5-10% with only 5 interactions
over competitive baselines on the Caltech-UCSDBirds, CheXpert and OAI datasets.Comment: To appear at AAAI 202
Robust outlier detection by de-biasing VAE likelihoods
Deep networks often make confident, yet, incorrect, predictions when tested
with outlier data that is far removed from their training distributions.
Likelihoods computed by deep generative models (DGMs) are a candidate metric
for outlier detection with unlabeled data. Yet, previous studies have shown
that DGM likelihoods are unreliable and can be easily biased by simple
transformations to input data. Here, we examine outlier detection with
variational autoencoders (VAEs), among the simplest of DGMs. We propose novel
analytical and algorithmic approaches to ameliorate key biases with VAE
likelihoods. Our bias corrections are sample-specific, computationally
inexpensive, and readily computed for various decoder visible distributions.
Next, we show that a well-known image pre-processing technique -- contrast
stretching -- extends the effectiveness of bias correction to further improve
outlier detection. Our approach achieves state-of-the-art accuracies with nine
grayscale and natural image datasets, and demonstrates significant advantages
-- both with speed and performance -- over four recent, competing approaches.
In summary, lightweight remedies suffice to achieve robust outlier detection
with VAEs.Comment: To appear at CVPR 2022. 20 pages and 19 figure
Hyperon signatures in the PANDA experiment at FAIR
We present a detailed simulation study of the signatures from the sequential decays of the triple-strange pbar p -> Ω+Ω- -> K+ΛbarK- Λ -> K+pbarπ+K-pπ- process in the PANDA central tracking system with focus on hit patterns and precise time measurement. We present a systematic approach for studying physics channels at the detector level and develop input criteria for tracking algorithms and trigger lines. Finally, we study the beam momentum dependence on the reconstruction efficiency for the PANDA detector