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Found 7 papers, 2 papers with code
S3TA: A Soft, Spatial, Sequential, Top-Down Attention Model
We present a soft, spatial, sequential, top-down attention model (S3TA).
The Effectiveness of Memory Replay in Large Scale Continual Learning
However, a degraded performance is observed for ER with small memory.
Optimization and Generalization of Regularization-Based Continual Learning: a Loss Approximation Viewpoint
This problem is often referred to as catastrophic forgetting, a key challenge in continual learning of neural networks.
Towards Robust Image Classification Using Sequential Attention Models
In this paper we propose to augment a modern neural-network architecture with an attention model inspired by human perception.
Orthogonal Gradient Descent for Continual Learning
In this paper, we propose to address this issue from a parameter space perspective and study an approach to restrict the direction of the gradient updates to avoid forgetting previously-learned data.
Quantum adiabatic machine learning with zooming
The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a new class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
Inspired by recent work in attention models for image captioning and question answering, we present a soft attention model for the reinforcement learning domain.
