Publications

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

Jithendaraa Subramanian

Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied … (voir plus)to the problem of inferring the marginal posterior distribution over the directed acyclic graph (DAG) of a Bayesian Network, given a dataset of observations. Based on recent advances extending this framework to non-discrete sample spaces, we propose in this paper to approximate the joint posterior over not only the structure of a Bayesian Network, but also the parameters of its conditional probability distributions. We use a single GFlowNet whose sampling policy follows a two-phase process: the DAG is first generated sequentially one edge at a time, and then the corresponding parameters are picked once the full structure is known. Since the parameters are included in the posterior distribution, this leaves more flexibility for the local probability models of the Bayesian Network, making our approach applicable even to non-linear models parametrized by neural networks. We show that our method, called JSP-GFN, offers an accurate approximation of the joint posterior, while comparing favorably against existing methods on both simulated and real data.

openreview.net

Joint Prompt Optimization of Stacked LLMs using Variational Inference

Alessandro Sordoni

Eric Yuan

Xingdi Yuan

Marc-Alexandre Côté

Matheus Pereira

Adam Trischler

Ziang Xiao

Arian Hosseini

Friederike Niedtner

Nicolas Le Roux

Large language models (LLMs) can be seen as atomic units of computation mapping sequences to a distribution over sequences. Thus, they can b… (voir plus)e seen as stochastic language layers in a language network, where the learnable parameters are the natural language prompts at each layer. By stacking two such layers and feeding the output of one layer to the next, we obtain a Deep Language Network (DLN). We first show how to effectively perform prompt optimization for a 1-Layer language network (DLN-1). Then, we present an extension that applies to 2-layer DLNs (DLN-2), where two prompts must be learned. The key idea is to consider the output of the first layer as a latent variable, which requires inference, and prompts to be learned as the parameters of the generative distribution. We first test the effectiveness of DLN-1 in multiple reasoning and natural language understanding tasks. Then, we show that DLN-2 can reach higher performance than a single layer, showing promise that we might reach comparable performance to GPT-4, even when each LLM in the network is smaller and less powerful.

openreview.net

Language Model Alignment with Elastic Reset

Michael Noukhovitch

Samuel Lavoie

Florian Strub

Aaron Courville

Finetuning language models with reinforcement learning (RL), e.g. from human feedback (HF), is a prominent method for alignment. But optimiz… (voir plus)ing against a reward model can improve on reward while degrading performance in other areas, a phenomenon known as reward hacking, alignment tax, or language drift. First, we argue that commonly-used test metrics are insufficient and instead measure how different algorithms tradeoff between reward and drift. The standard method modified the reward with a Kullback-Lieber (KL) penalty between the online and initial model. We propose Elastic Reset, a new algorithm that achieves higher reward with less drift without explicitly modifying the training objective. We periodically reset the online model to an exponentially moving average (EMA) of itself, then reset the EMA model to the initial model. Through the use of an EMA, our model recovers quickly after resets and achieves higher reward with less drift in the same number of steps. We demonstrate that fine-tuning language models with Elastic Reset leads to state-of-the-art performance on a small scale pivot-translation benchmark, outperforms all baselines in a medium-scale RLHF-like IMDB mock sentiment task and leads to a more performant and more aligned technical QA chatbot with LLaMA-7B. Code available at github.com/mnoukhov/elastic-reset.

openreview.net

Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions

Stefano Massaroli

Michael Poli

Daniel Y Fu

Hermann Kumbong

Rom Nishijima Parnichkun

Aman Timalsina

David W. Romero

Quinn McIntyre

Beidi Chen

Atri Rudra

Ce Zhang

Christopher Re

Stefano Ermon

Yoshua Bengio

Recent advances in attention-free sequence models rely on convolutions as alternatives to the attention operator at the core of Transformers… (voir plus). In particular, long convolution sequence models have achieved state-of-the-art performance in many domains, but incur a significant cost during auto-regressive inference workloads -- naively requiring a full pass (or caching of activations) over the input sequence for each generated token -- similarly to attention-based models. In this paper, we seek to enable

openreview.net

Learning better with Dale’s Law: A Spectral Perspective

Learning Reliable Logical Rules with SATNet

Zhaoyu Li

Jinpei Guo

Yuhe Jiang

Xujie Si

openreview.net

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets

Hanjun Dai

Ling Pan

Lie Point Symmetry and Physics-Informed Networks

Tara Akhound-Sadegh

Laurence Perreault-Levasseur

Johannes Brandstetter

Max Welling

Siamak Ravanbakhsh

Symmetries have been leveraged to improve the generalization of neural networks through different mechanisms from data augmentation to equiv… (voir plus)ariant architectures. However, despite their potential, their integration into neural solvers for partial differential equations (PDEs) remains largely unexplored. We explore the integration of PDE symmetries, known as Lie point symmetries, in a major family of neural solvers known as physics-informed neural networks (PINNs). We propose a loss function that informs the network about Lie point symmetries in the same way that PINN models try to enforce the underlying PDE through a loss function. Intuitively, our symmetry loss ensures that the infinitesimal generators of the Lie group conserve the PDE solutions.. Effectively, this means that once the network learns a solution, it also learns the neighbouring solutions generated by Lie point symmetries. Empirical evaluations indicate that the inductive bias introduced by the Lie point symmetries of the PDEs greatly boosts the sample efficiency of PINNs.

openreview.net

Maximum State Entropy Exploration using Predecessor and Successor Representations

Arnav Kumar Jain

Lucas Lehnert

Irina Rish

Glen Berseth

Animals have a developed ability to explore that aids them in important tasks such as locating food, exploring for shelter, and finding misp… (voir plus)laced items. These exploration skills necessarily track where they have been so that they can plan for finding items with relative efficiency. Contemporary exploration algorithms often learn a less efficient exploration strategy because they either condition only on the current state or simply rely on making random open-loop exploratory moves. In this work, we propose

openreview.net

Multi-Head Adapter Routing for Cross-Task Generalization

Lucas Caccia

Edoardo Ponti

Zhan Su

Matheus Pereira

Nicolas Le Roux

Alessandro Sordoni

Parameter-efficient fine-tuning (PEFT) for cross-task generalization consists in pre-training adapters on a multi-task training set before f… (voir plus)ew-shot adaptation to test tasks. Polytropon [Ponti et al., 2023] (

openreview.net

Neural Graph Generation from Graph Statistics.

Kiarash Zahirnia

Yaochen Hu

Mark Coates

Oliver Schulte

openreview.net

Neural Graph Generation from Graph Statistics

Kiarash Zahirnia

Yaochen Hu

Mark Coates

Oliver Schulte