841 citations · 2.6k across the 48 of their papers we have counts for
20 papers · 1 filter
A General Framework for Inference-time Scaling and Steering of Diffusion Models
Raghav Singhal, Zachary Horvitz, Ryan Teehan +4
Diffusion models produce impressive results in modalities ranging from images and video to protein design and text. However, generating samples with user-specified properties remai…
Integrating Present and Past in Unsupervised Continual Learning
Yipeng Zhang, Laurent Charlin, Richard Zemel +1
We formulate a unifying framework for unsupervised continual learning (UCL), which disentangles learning objectives that are specific to the present and the past data, encompassing…
Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training
Yanlai Yang, Matt Jones, Michael C. Mozer +1
We explore the training dynamics of neural networks in a structured non-IID setting where documents are presented cyclically in a fixed, repeated sequence. Typically, networks suff…
Gaussian-Bernoulli RBMs Without Tears
Renjie Liao, Simon Kornblith, Mengye Ren +2
We revisit the challenging problem of training Gaussian-Bernoulli restricted Boltzmann machines (GRBMs), introducing two innovations. We propose a novel Gibbs-Langevin sampling alg…
Scaling Forward Gradient With Local Losses
Mengye Ren, Simon Kornblith, Renjie Liao +1
Forward gradient learning computes a noisy directional gradient and is a biologically plausible alternative to backprop for learning deep neural networks. However, the standard for…
Learning to Reason With Relational Abstractions
Andrew J. Nam, Mengye Ren, Chelsea Finn +1
Large language models have recently shown promising progress in mathematical reasoning when fine-tuned with human-generated sequences walking through a sequence of solution steps.…
Cost-Efficient Online Hyperparameter Optimization
Jingkang Wang, Mengye Ren, Ilija Bogunovic +2
Recent work on hyperparameters optimization (HPO) has shown the possibility of training certain hyperparameters together with regular parameters. However, these online HPO algorith…
Adversarial Attacks On Multi-Agent Communication
James Tu, Tsunhsuan Wang, Jingkang Wang +3
Growing at a fast pace, modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems. Sharing information and distributi…
Probing Few-Shot Generalization with Attributes
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang +5
Despite impressive progress in deep learning, generalizing far beyond the training distribution is an important open challenge. In this work, we consider few-shot classification, a…
LoCo: Local Contrastive Representation Learning
Yuwen Xiong, Mengye Ren, Raquel Urtasun
Deep neural nets typically perform end-to-end backpropagation to learn the weights, a procedure that creates synchronization constraints in the weight update step across layers and…
Wandering Within a World: Online Contextualized Few-Shot Learning
Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer +1
We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. In this s…
Learning to Remember from a Multi-Task Teacher
Yuwen Xiong, Mengye Ren, Raquel Urtasun
Recent studies on catastrophic forgetting during sequential learning typically focus on fixing the accuracy of the predictions for a previously learned task. In this paper we argue…
Incremental Few-Shot Learning with Attention Attractor Networks
Mengye Ren, Renjie Liao, Ethan Fetaya +1
Machine learning classifiers are often trained to recognize a set of pre-defined classes. However, in many applications, it is often desirable to have the flexibility of learning a…
Graph HyperNetworks for Neural Architecture Search
Chris Zhang, Mengye Ren, Raquel Urtasun
Neural architecture search (NAS) automatically finds the best task-specific neural network topology, outperforming many manual architecture designs. However, it can be prohibitivel…
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
Yuhuai Wu, Mengye Ren, Renjie Liao +1
Careful tuning of the learning rate, or even schedules thereof, can be crucial to effective neural net training. There has been much recent interest in gradient-based meta-optimiza…
Meta-Learning for Semi-Supervised Few-Shot Classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi +5
In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has…
Learning to Reweight Examples for Robust Deep Learning
Mengye Ren, Wenyuan Zeng, Bin Yang +1
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit t…
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
Mengye Ren, Renjie Liao, Raquel Urtasun +2
Normalization techniques have only recently begun to be exploited in supervised learning tasks. Batch normalization exploits mini-batch statistics to normalize the activations. Thi…
End-to-End Instance Segmentation with Recurrent Attention
Mengye Ren, Richard S. Zemel
While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to diffe…
Exploring Models and Data for Image Question Answering
Mengye Ren, Ryan Kiros, Richard Zemel
This work aims to address the problem of image-based question-answering (QA) with new models and datasets. In our work, we propose to use neural networks and visual semantic embedd…