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Found 8 papers, 3 papers with code
VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting
Extensive experiments on data sources obtained in Delhi demonstrate that the proposed adaptive attention based Bidirectional LSTM Network outperforms several baselines for classification and regression models.
Beyond VQA: Generating Multi-word Answer and Rationale to Visual Questions
To take this a step forward, we introduce a new task: ViQAR (Visual Question Answering and Reasoning), wherein a model must generate the complete answer and a rationale that seeks to justify the generated answer.
Natural Attribute-based Shift Detection
Despite the impressive performance of deep networks in vision, language, and healthcare, unpredictable behaviors on samples from the distribution different than the training distribution cause severe problems in deployment.
ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes
ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds.
Towards the Practical Utility of Federated Learning in the Medical Domain
We evaluate six FL algorithms designed for addressing data heterogeneity among clients, and a hybrid algorithm combining the strengths of two representative FL algorithms.
Task Agnostic and Post-hoc Unseen Distribution Detection
Despite the recent advances in out-of-distribution(OOD) detection, anomaly detection, and uncertainty estimation tasks, there do not exist a task-agnostic and post-hoc approach.
Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels
Automatic deep learning-based examination of ECG signals can lead to inaccurate diagnosis, and manual analysis involves rejection of noisy ECG samples by clinicians, which might cost extra time.
Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity
To analyze this, we first train a classifier that identifies machine-written sentences, and observe that the linguistic features of the sentences identified as written by a machine are significantly different from those of human-written sentences.
