arXiv:2510.08657v1 Announce Type: new Abstract: Real-world time series are influenced by numerous factors and exhibit complex non-stationary characteristics. Non-stationarity can lead to distribution shifts, where the statistical properties of time series change over time, negatively impacting model performance. Several instance…
arXiv:2506.03355v2 Announce Type: replace Abstract: Adversarial input attacks can cause a significant shift of CLIP embeddings. This can affect the downstream robustness of models incorporating CLIP in the pipeline, such as text-to-image generative models or large vision language models. While…
arXiv:2311.01591v4 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have achieved state-of-the-art results in many relevant tasks where decisions might disproportionately impact specific communities. However, existing work on fair GNNs often assumes that either sensitive attributes are fully observed or…
arXiv:2502.11411v2 Announce Type: replace Abstract: Large language models (LLMs) are highly sensitive to even small amounts of unsafe training data, making effective detection and filtering essential for trustworthy model development. Current state-of-the-art (SOTA) detection approaches primarily rely on moderation classifiers,…
arXiv:2506.03355v2 Announce Type: replace Abstract: Adversarial input attacks can cause a significant shift of CLIP embeddings. This can affect the downstream robustness of models incorporating CLIP in the pipeline, such as text-to-image generative models or large vision language models. While…
arXiv:2311.01591v4 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have achieved state-of-the-art results in many relevant tasks where decisions might disproportionately impact specific communities. However, existing work on fair GNNs often assumes that either sensitive attributes are fully observed or…
arXiv:2502.11411v2 Announce Type: replace Abstract: Large language models (LLMs) are highly sensitive to even small amounts of unsafe training data, making effective detection and filtering essential for trustworthy model development. Current state-of-the-art (SOTA) detection approaches primarily rely on moderation classifiers,…
Google AI Research team has brought a production shift in Voice Search by introducing Speech-to-Retrieval (S2R). S2R maps a spoken query directly to an embedding and retrieves information without first converting speech to text. The Google team positions S2R as…
Google AI Research team has brought a production shift in Voice Search by introducing Speech-to-Retrieval (S2R). S2R maps a spoken query directly to an embedding and retrieves information without first converting speech to text. The Google team positions S2R as…
Google AI Research team has brought a production shift in Voice Search by introducing Speech-to-Retrieval (S2R). S2R maps a spoken query directly to an embedding and retrieves information without first converting speech to text. The Google team positions S2R as…
