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Found 10 papers, 2 papers with code
Model Predictive Simulation Using Structured Graphical Models and Transformers
The transformer baseline is based on the MTR model, which predicts multiple future trajectories conditioned on the past trajectories and static road layout features.
Graph schemas as abstractions for transfer learning, inference, and planning
Graph schemas can be learned in far fewer episodes than previous baselines, and can model and plan in a few steps in novel variations of these tasks.
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX
PGMax is an open-source Python package for (a) easily specifying discrete Probabilistic Graphical Models (PGMs) as factor graphs; and (b) automatically running efficient and scalable loopy belief propagation (LBP) in JAX.
Better Together: Resnet-50 accuracy with $13 \times $ fewer parameters and at $3 \times $ speed
Our one-shot learning paradigm trains both the original and the smaller networks together.
On sampling from data with duplicate records
Our goal is to develop a procedure that samples uniformly from the set of entities present in the database in the presence of duplicates.
Record fusion: A learning approach
We use this feature vector alongwith the ground-truth information to learn a classifier for each of the attributes of the database.
Adjoined Networks: A Training Paradigm with Applications to Network Compression
In this paper, we introduce Adjoined Networks, or AN, a learning paradigm that trains both the original base network and the smaller compressed network together.
Semi-supervised clustering for de-duplication
In this work, we view de-duplication as a clustering problem where the goal is to put records corresponding to the same physical entity in the same cluster and putting records corresponding to different physical entities into different clusters.
Provably noise-robust, regularised $k$-means clustering
We focus on the $k$-means objective and we prove that the regularised version of $k$-means is NP-Hard even for $k=1$.
Clustering with Same-Cluster Queries
We show that there is a trade off between computational complexity and query complexity; We prove that for the case of $k$-means clustering (i. e., when the expert conforms to a solution of $k$-means), having access to relatively few such queries allows efficient solutions to otherwise NP hard problems.
