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ibot: Image bert pre-training with online tokenizer
The success of language Transformers is primarily attributed to the pretext task of masked
language modeling (MLM), where texts are first tokenized into semantically meaningful …
language modeling (MLM), where texts are first tokenized into semantically meaningful …
Delving into out-of-distribution detection with vision-language representations
Recognizing out-of-distribution (OOD) samples is critical for machine learning systems
deployed in the open world. The vast majority of OOD detection methods are driven by a …
deployed in the open world. The vast majority of OOD detection methods are driven by a …
Last layer re-training is sufficient for robustness to spurious correlations
Neural network classifiers can largely rely on simple spurious features, such as
backgrounds, to make predictions. However, even in these cases, we show that they still …
backgrounds, to make predictions. However, even in these cases, we show that they still …
On the opportunities and risks of foundation models
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
Rethinking spatial dimensions of vision transformers
Abstract Vision Transformer (ViT) extends the application range of transformers from
language processing to computer vision tasks as being an alternative architecture against …
language processing to computer vision tasks as being an alternative architecture against …
Causal machine learning: A survey and open problems
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …
that formalize the data-generation process as a structural causal model (SCM). This …
On feature learning in the presence of spurious correlations
Deep classifiers are known to rely on spurious features—patterns which are correlated with
the target on the training data but not inherently relevant to the learning problem, such as the …
the target on the training data but not inherently relevant to the learning problem, such as the …
Swad: Domain generalization by seeking flat minima
Abstract Domain generalization (DG) methods aim to achieve generalizability to an unseen
target domain by using only training data from the source domains. Although a variety of DG …
target domain by using only training data from the source domains. Although a variety of DG …
A fine-grained analysis on distribution shift
Robustness to distribution shifts is critical for deploying machine learning models in the real
world. Despite this necessity, there has been little work in defining the underlying …
world. Despite this necessity, there has been little work in defining the underlying …
Change is hard: A closer look at subpopulation shift
Machine learning models often perform poorly on subgroups that are underrepresented in
the training data. Yet, little is understood on the variation in mechanisms that cause …
the training data. Yet, little is understood on the variation in mechanisms that cause …