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Found 13 papers, 5 papers with code
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware
Efficient large-scale neural network training and inference on commodity CPU hardware is of immense practical significance in democratizing deep learning (DL) capabilities.
Ranked #2 on
Node Classification
on Yelp-Fraud
Distributed SLIDE: Enabling Training Large Neural Networks on Low Bandwidth and Simple CPU-Clusters via Model Parallelism and Sparsity
We show that with reduced communication, due to sparsity, we can train close to a billion parameter model on simple 4-16 core CPU nodes connected by basic low bandwidth interconnect.
IRLI: Iterative Re-partitioning for Learning to Index
Neural models have transformed the fundamental information retrieval problem of mapping a query to a giant set of items.
A Truly Constant-time Distribution-aware Negative Sampling
Softmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval.
A Tale of Two Efficient and Informative Negative Sampling Distributions
Unfortunately, due to the dynamically updated parameters and data samples, there is no sampling scheme that is provably adaptive and samples the negative classes efficiently.
SOLAR: Sparse Orthogonal Learned and Random Embeddings
The label vectors are random, sparse, and near-orthogonal by design, while the query vectors are learned and sparse.
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
Our largest model has 6. 4 billion parameters and trains in less than 35 hours on a single p3. 16x machine.
Fast Processing and Querying of 170TB of Genomics Data via a Repeated And Merged BloOm Filter (RAMBO)
Interestingly, it is a count-min sketch type arrangement of a membership testing utility (Bloom Filter in our case).
RAMBO: Repeated And Merged Bloom Filter for Multiple Set Membership Testing (MSMT) in Sub-linear time
A simple array of Bloom Filters can achieve that.
SDM-Net: A Simple and Effective Model for Generalized Zero-Shot Learning
Zero-Shot Learning (ZSL) is a classification task where we do not have even a single training labeled example from a set of unseen classes.
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems
On the same CPU hardware, SLIDE is over 10x faster than TF.
Extreme Classification in Log Memory
With MACH we can train ODP dataset with 100, 000 classes and 400, 000 features on a single Titan X GPU, with the classification accuracy of 19. 28%, which is the best-reported accuracy on this dataset.
Mimicking actions is a good strategy for beginners: Fast Reinforcement Learning with Expert Action Sequences
Imitation Learning is the task of mimicking the behavior of an expert player in a Reinforcement Learning(RL) Environment to enhance the training of a fresh agent (called novice) beginning from scratch.
