Search Results for author:
Found 9 papers, 2 papers with code
Communication-efficient Distributed Learning for Large Batch Optimization
In this paper, we propose new gradient compression methods for large batch optimization, JointSpar and its variant JointSpar-LARS with layerwise adaptive learning rates, that jointly reduce both the computation and the communication cost.
Transformer with Memory Replay
Transformers achieve state-of-the-art performance for natural language processing tasks by pre-training on large-scale text corpora.
Adam with Bandit Sampling for Deep Learning
In this paper, we propose a generalization of Adam, called Adambs, that allows us to also adapt to different training examples based on their importance in the model's convergence.
QuickSel: Quick Selectivity Learning with Mixture Models
Estimating the selectivity of a query is a key step in almost any cost-based query optimizer.
Databases
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase.
A Bandit Approach to Maximum Inner Product Search
There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search (MIPS).
Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets
We also show that, for the general case of (smooth) non-convex functions, FW with line search converges with high probability to a stationary point at a rate of $O\left(\frac{1}{t}\right)$, as long as the constraint set is strongly convex -- one of the fastest convergence rates in non-convex optimization.
Database Learning: Toward a Database that Becomes Smarter Every Time
Also, processing more queries should continuously enhance our knowledge of the underlying distribution, and hence lead to increasingly faster response times for future queries.
Active Learning for Crowd-Sourced Databases
Based on this observation, we present two new active learning algorithms to combine humans and algorithms together in a crowd-sourced database.
