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Found 10 papers, 2 papers with code
C3SASR: Cheap Causal Convolutions for Self-Attentive Sequential Recommendation
We evaluate the effectiveness of the proposed model in terms of both accurate and calibrated sequential recommendation.
Teaching Neural Module Networks to Do Arithmetic
Answering complex questions that require multi-step multi-type reasoning over raw text is challenging, especially when conducting numerical reasoning.
DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation
Compared with the field of object grasping with parallel grippers, dexterous grasping is very under-explored, partially owing to the lack of a large-scale dataset.
Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild
We for the first time propose a point cloud based hand joint tracking network, HandTrackNet, to estimate the inter-frame hand joint motion.
A Competitive Method for Dog Nose-print Re-identification
Vision-based pattern identification (such as face, fingerprint, iris etc.)
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity.
Long-Tail Session-based Recommendation from Calibration
Accurate predictions in session-based recommendations have progressed, but a few studies have focused on skewed recommendation lists caused by popularity bias.
Projective Manifold Gradient Layer for Deep Rotation Regression
Regressing rotations on SO(3) manifold using deep neural networks is an important yet unsolved problem.
HetMAML: Task-Heterogeneous Model-Agnostic Meta-Learning for Few-Shot Learning Across Modalities
To deal with task heterogeneity and promote fast within-task adaptions for each type of tasks, in this paper, we propose HetMAML, a task-heterogeneous model-agnostic meta-learning framework, which can capture both the type-specific and globally shared knowledge and can achieve the balance between knowledge customization and generalization.
Towards a Unified Evaluation of Explanation Methods without Ground Truth
This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges.
