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A training instability where gradients grow excessively, causing large updates and numerical overflow in neural networks.
DESCRIPTION
Exploding gradients occur when backpropagated signals amplify through layers, leading to enormous parameter updates and unstable training. RNNs and deep networks with poorly initialized weights or high learning rates are prone to this issue. Symptoms include NaNs, divergence, and erratic loss. Common remedies are gradient clipping, better initialization, normalization, and careful learning-rate schedules. Recognizing and mitigating exploding gradients is essential for stable model convergence, especially in deep or recurrent architectures.
ALTERNATIVE DEFINITIONS
Wikipedia
The opposite problem, in which gradients grow exponentially, is called the exploding gradient problem.
Defines exploding gradients as the counterpart to vanishing gradients.
Pascanu, Mikolov, Bengio (2013)
The exploding and vanishing gradient problem refers to the difficulties in training RNNs where gradients can grow or shrink exponentially.
Formalizes the phenomenon in the context of RNN training difficulties.
Goodfellow, Bengio, Courville
Exploding gradients can result in very large parameter updates, often causing numerical overflow and unstable learning.
Emphasizes practical consequences during optimization.
RELATED TERMS
Gradient Clipping Learning Rate Feature Scaling Recurrent Neural Network LSTM Batch Normalization Weight Initialization
COMPETING TERMS
RELATIONS
- Broader: Gradient
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Vanishing gradient problem
- arXiv - Foundational RNN gradient paper
- PyTorch - Gradient clipping utility
TOP QUESTIONS
What causes the Exploding Gradient problem?
Poor initialization, large learning rates, and multiplicative effects across deep or recurrent layers amplify gradients.
Category: Requirements
How do you detect Exploding Gradients?
Monitor gradient norms, watch for NaNs or sudden loss spikes, and inspect parameter updates during training.
Category: Testing
How can Exploding Gradients be mitigated?
Apply gradient clipping, use better initialization and normalization, and tune learning-rate schedules conservatively.
Category: Management
What is the main risk of Exploding Gradients?
Training instability and divergence can waste compute and produce unreliable models.
Category: Risks
What’s a common misunderstanding about Exploding Gradients?
Some think only RNNs suffer; deep feedforward and transformer models can also experience instability without proper measures.
Category: Misunderstanding
How do Exploding Gradients relate to Vanishing Gradients?
They are dual phenomena arising from gradient propagation; both impede effective learning.
Category: Alternatives
What tools help manage Exploding Gradients?
Framework utilities for clipping, logging gradient norms, and schedulers make mitigation straightforward.
Category: Example
What is currently a hot topic around Exploding Gradients?
Stable optimization techniques for very deep networks and large-scale training remain active areas.
Category: Topic
Where should I start learning about Exploding Gradients?
Study the Pascanu et al. paper and practical tutorials using PyTorch or TensorFlow.
Category: Example