RealTruth/secgpt-Q8_0-GGUF
Text Generation
•
Updated
•
278
•
1
rabbit
Zhaojjiahui
·
AI & ML interests
None yet
Recent Activity
liked
a model
2 days ago
RealTruth/secgpt-Q8_0-GGUF
liked
a dataset
2 days ago
clouditera/security-paper-datasets
liked
a model
21 days ago
Qwen/Qwen2.5-1.5B-Instruct
Organizations
Zhaojjiahui's activity
liked
a
model
2 days ago
liked
a
dataset
2 days ago
liked
a
model
21 days ago
liked
a
model
28 days ago
liked
2
models
about 1 month ago
reacted to
singhsidhukuldeep's
post with 👀
about 1 month ago
Post
910
It's always exciting to revisit Google's DCN paper—impractical but good!
Deep & Cross Network (DCN) - a groundbreaking approach to click-through rate prediction that's revolutionizing digital advertising!
Key Innovation:
DCN introduces a novel cross-network architecture that automatically learns feature interactions without manual engineering. What sets it apart is its ability to explicitly model bounded-degree feature crossings while maintaining the power of deep neural networks.
Technical Deep Dive:
- The architecture combines a cross network with a deep network in parallel.
- The cross network performs automatic feature crossing at each layer.
- The embedding layer transforms sparse categorical features into dense vectors.
- Cross layers use a unique formula that enables efficient high-degree polynomial feature interactions.
- Memory-efficient design with linear complexity O(d) in the input dimension.
Performance Highlights:
- Outperforms traditional DNN models with 60% less memory usage.
- Achieved 0.4419 logloss on the Criteo Display Ads dataset.
- Consistently performs better than state-of-the-art models like Deep Crossing and Factorization Machines.
- Exceptional performance on non-CTR tasks like Forest Covertype (97.40% accuracy).
Under the Hood:
- Uses embedding vectors of dimension 6 × (category cardinality)^1/4.
- Implements batch normalization and the Adam optimizer.
- The cross network depth determines the highest polynomial degree of feature interactions.
- An efficient projection mechanism reduces cubic computational cost to linear.
- Parameter sharing enables better generalization to unseen feature interactions.
Key Advantages:
1. No manual feature engineering required.
2. Explicit feature crossing at each layer.
3. Highly memory-efficient.
4. Scalable to web-scale data.
5. Robust performance across different domains.
Thoughts on how this could transform digital advertising?
Deep & Cross Network (DCN) - a groundbreaking approach to click-through rate prediction that's revolutionizing digital advertising!
Key Innovation:
DCN introduces a novel cross-network architecture that automatically learns feature interactions without manual engineering. What sets it apart is its ability to explicitly model bounded-degree feature crossings while maintaining the power of deep neural networks.
Technical Deep Dive:
- The architecture combines a cross network with a deep network in parallel.
- The cross network performs automatic feature crossing at each layer.
- The embedding layer transforms sparse categorical features into dense vectors.
- Cross layers use a unique formula that enables efficient high-degree polynomial feature interactions.
- Memory-efficient design with linear complexity O(d) in the input dimension.
Performance Highlights:
- Outperforms traditional DNN models with 60% less memory usage.
- Achieved 0.4419 logloss on the Criteo Display Ads dataset.
- Consistently performs better than state-of-the-art models like Deep Crossing and Factorization Machines.
- Exceptional performance on non-CTR tasks like Forest Covertype (97.40% accuracy).
Under the Hood:
- Uses embedding vectors of dimension 6 × (category cardinality)^1/4.
- Implements batch normalization and the Adam optimizer.
- The cross network depth determines the highest polynomial degree of feature interactions.
- An efficient projection mechanism reduces cubic computational cost to linear.
- Parameter sharing enables better generalization to unseen feature interactions.
Key Advantages:
1. No manual feature engineering required.
2. Explicit feature crossing at each layer.
3. Highly memory-efficient.
4. Scalable to web-scale data.
5. Robust performance across different domains.
Thoughts on how this could transform digital advertising?
- 2 replies
liked
a
model
about 1 month ago
liked
a
dataset
about 1 month ago
liked
a
model
about 1 month ago
liked
4
models
about 2 months ago
liked
a
Space
about 2 months ago
liked
2
models
2 months ago
liked
a
Space
2 months ago
liked
a
Space
3 months ago
liked
a
model
3 months ago
