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Found 19 papers, 17 papers with code
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an unnormalized target distribution defined by a given reward or potential function over the full sequence.
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective
Adaptive gradient optimizers like Adam(W) are the default training algorithms for many deep learning architectures, such as transformers.
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC
Second-order methods such as KFAC can be useful for neural net training.
A Computational Framework for Solving Wasserstein Lagrangian Flows
The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry (kinetic energy), and the regularization of density paths (potential energy).
Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs
We present a one-shot method for compressing large labeled graphs called Random Edge Coding.
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds
Since accurate estimation of MI without density information requires a sample size exponential in the true MI, we assume either a single marginal or the full joint density information is known.
Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
Binary neural networks, i. e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep learning models on energy- and memory-limited devices.
Action Matching: Learning Stochastic Dynamics from Samples
Learning the continuous dynamics of a system from snapshots of its temporal marginals is a problem which appears throughout natural sciences and machine learning, including in quantum systems, single-cell biological data, and generative modeling.
Variational Model Inversion Attacks
In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy.
Compressing Multisets with Large Alphabets using Bits-Back Coding
Current methods which compress multisets at an optimal rate have computational complexity that scales linearly with alphabet size, making them too slow to be practical in many real-world settings.
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Naively applied, our schemes would require more initial bits than the standard bits-back coder, but we show how to drastically reduce this additional cost with couplings in the latent space.
Likelihood Ratio Exponential Families
The exponential family is well known in machine learning and statistical physics as the maximum entropy distribution subject to a set of observed constraints, while the geometric mixture path is common in MCMC methods such as annealed importance sampling.
Evaluating Lossy Compression Rates of Deep Generative Models
The field of deep generative modeling has succeeded in producing astonishingly realistic-seeming images and audio, but quantitative evaluation remains a challenge.
Implicit Autoencoders
In this paper, we describe the "implicit autoencoder" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions.
StarCraft II: A New Challenge for Reinforcement Learning
Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain.
Ranked #1 on
Starcraft II
on MoveToBeacon
PixelGAN Autoencoders
In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code.
Ranked #10 on
Unsupervised Image Classification
on MNIST
Adversarial Autoencoders
In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution.
Ranked #7 on
Unsupervised MNIST
on MNIST
Winner-Take-All Autoencoders
In this paper, we propose a winner-take-all method for learning hierarchical sparse representations in an unsupervised fashion.
k-Sparse Autoencoders
Recently, it has been observed that when representations are learnt in a way that encourages sparsity, improved performance is obtained on classification tasks.
