Rif A. Saurous
Authored Publications
Sort By
MesaNet: Sequence Modelling by Locally Optimal test-Time Training
Songlin Yang
Razvan Pascanu
Alexander Meulemans
Seijin Kobayashi
Yanick Schimpf
João Sacramento
Maximilian Schlegel
Luca Versari
Oliver Sieberling
2025
Preview abstract
Sequence modeling is currently dominated by causal transformer architectures that
use softmax self-attention. Although widely adopted, transformers require scaling
memory and compute linearly during inference. A recent stream of work linearized
the softmax operation, resulting in powerful recurrent neural network (RNN)
models with constant memory and compute costs such as DeltaNet, Mamba or
xLSTM. These models can be unified by noting that their recurrent layer dynamics
can all be derived from an in-context regression objective, approximately optimized
through an online learning rule. Here, we join this line of work and introduce a
numerically stable, chunkwise parallelizable version of the recently proposed Mesa
layer (von Oswald et al., 2024), which could only run sequentially in time and was
therefore not scalable. This layer again stems from an in-context loss, but which is
now minimized to optimality at every time point using a fast conjugate gradient
solver. Through an extensive suite of experiments study up to the billion-parameter
scale, we show that optimal test-time training enables reaching lower language
modeling perplexity and higher downstream benchmark performance than previous
RNNs, especially on tasks requiring long context understanding. This performance
gain comes at the cost of additional flops spent during inference time. Our results
are therefore intriguingly related to recent trends of increasing test-time compute to
improve performance – here by spending compute to solve sequential optimization
problems within the neural network itself.
View details
Sequential Monte Carlo Learning for Time Series Structure Discovery
Feras Saad
Matthew D. Hoffman
Vikash Mansinghka
Proceedings of the 40th International Conference on Machine Learning (2023), pp. 29473-29489
Preview abstract
This paper presents a new approach to automatically discovering accurate
models of complex time series data. Working within a Bayesian nonparametric
prior over a symbolic space of Gaussian process time series models, we
present a novel structure learning algorithm that integrates sequential
Monte Carlo (SMC) and involutive MCMC for highly effective posterior
inference. Our method can be used both in "online'' settings, where new
data is incorporated sequentially in time, and in "offline'' settings, by
using nested subsets of historical data to anneal the posterior. Empirical
measurements on a variety of real-world time series show that our method
can deliver 10x--100x runtime speedups over previous MCMC and greedy-search
structure learning algorithms for the same model family. We use our method
to perform the first large-scale evaluation of Gaussian process time series
structure learning on a widely used benchmark of 1,428 monthly econometric
datasets, showing that our method discovers sensible models that deliver
more accurate point forecasts and interval forecasts over multiple horizons
as compared to prominent statistical and neural baselines that struggle on
this challenging data.
View details
Automatically batching control-intensive programs for modern accelerators
Alexey Radul
Dougal Maclaurin
Matthew D. Hoffman
Third Conference on Systems and Machine Learning, Austin, TX (2020)
Preview abstract
We present a general approach to batching arbitrary computations for
GPU and TPU accelerators. We demonstrate the effectiveness of our
method with orders-of-magnitude speedups on the No U-Turn Sampler
(NUTS), a workhorse algorithm in Bayesian statistics. The central
challenge of batching NUTS and other Markov chain Monte Carlo
algorithms is data-dependent control flow and recursion. We overcome
this by mechanically transforming a single-example implementation into
a form that explicitly tracks the current program point for each batch
member, and only steps forward those in the same place. We present
two different batching algorithms: a simpler, previously published one
that inherits recursion from the host Python, and a more complex,
novel one that implmenents recursion directly and can batch across it.
We implement these batching methods as a general program
transformation on Python source. Both the batching system and the
NUTS implementation presented here are available as part of the
popular TensorFlow Probability software package.
View details
Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision
Proceedings of ICASSP 2020 (2020) (to appear)
Preview abstract
Humans do not acquire perceptual abilities like we train machines. While machine learning algorithms typically operate on large collections of randomly-chosen, explicitly-labeled examples, human acquisition relies far greater on multimodal unsupervised learning (as infants) and active learning (as children). With this motivation, we present a learning framework for sound representation and recognition that combines (i) a self-supervised objective based on a general notion of unimodal and cross-modal coincidence, (ii) a novel clustering objective that reflects our need to impose categorical structure on our experiences, and (iii) a cluster-based active learning procedure that solicits targeted weak supervision to consolidate hypothesized categories into relevant semantic classes. By jointly training a single sound embedding/clustering/classification network according to these criteria, we achieve a new state-of-the-art unsupervised audio representation and demonstrate
up to 20-fold reduction in labels required to reach a desired classification performance.
View details
Large-Scale Weakly-Supervised Content Embeddingsfor Music Recommendation and Tagging
Qingqing Huang
Li Zhang
John Roberts Anderson
ICASSP 2020 (2020)
Preview abstract
We explore content-based representation learning strategies tailored for
large-scale, uncurated music collections that afford only weak supervision
through unstructured natural language metadata and co-listen statistics. At the
core is a hybrid training scheme that uses classification and metric learning
losses to incorporate both metadata-derived text labels and aggregate co-listen
supervisory signals into a single convolutional model. The resulting joint text
and audio content embedding defines a similarity metric and supports prediction
of semantic text labels using a vocabulary of unprecedented granularity, which
we refine using a novel word-sense disambiguation procedure. As input to simple
classifier architectures, our representation achieves state-of-the-art
performance on two music tagging benchmarks.
View details
Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility
Luyang Liu
Xue Ben
Shawn O'Banion
Matthew D. Hoffman
medRxiv, https://www.medrxiv.org/content/10.1101/2020.08.06.20169664v1.full (2020)
Preview abstract
In order to prepare for and control the continued spread of the COVID-19 pandemic while minimizing its economic impact, the world needs to be able to estimate and predict COVID-19’s spread.
Unfortunately, we cannot directly observe the prevalence or growth rate of COVID-19; these must be inferred using some kind of model.
We propose a hierarchical Bayesian extension to the classic susceptible-exposed-infected-removed (SEIR) compartmental model that adds compartments to account for isolation and death and allows the infection rate to vary as a function of both mobility data collected from mobile phones and a latent time-varying factor that accounts for changes in behavior not captured by mobility data. Since confirmed-case data is unreliable, we infer the model’s parameters conditioned on deaths data. We replace the exponential-waiting-time assumption of classic compartmental models with Erlang distributions, which allows for a more realistic model of the long lag between exposure and death. The mobility data gives us a leading indicator that can quickly detect changes in the pandemic’s local growth rate and forecast changes in death rates weeks ahead of time. This is an analysis of observational data, so any causal interpretations of the model's inferences should be treated as suggestive at best; nonetheless, the model’s inferred relationship between different kinds of trips and the infection rate do suggest some possible hypotheses about what kinds of activities might contribute most to COVID-19’s spread.
View details
Differentiable Consistency Constraints for Improved Deep Speech Enhancement
Jeremy Thorpe
Michael Chinen
IEEE International Conference on Acoustics, Speech, and Signal Processing (2019)
Preview abstract
In recent years, deep networks have led to dramatic improvements in speech enhancement by framing it as a data-driven pattern recognition problem. In many modern enhancement systems, large amounts of data are used to train a deep network to estimate masks for complex-valued short-time Fourier transforms (STFTs) to suppress noise and preserve speech. However, current masking approaches often neglect two important constraints: STFT consistency and mixture consistency. Without STFT consistency, the system’s output is not necessarily the STFT of a time-domain signal, and without mixture consistency, the sum of the estimated sources does not necessarily equal the input mixture. Furthermore, the only previous approaches that apply mixture consistency use real-valued masks; mixture consistency has been ignored for complex-valued masks. In this paper, we show that STFT consistency and mixture consistency can be jointly imposed by adding simple differentiable projection layers to the enhancement network. These layers are compatible with real or complex-valued masks. Using both of these constraints with complex-valued masks provides a 0.7 dB increase in scale-invariant signal-to-distortion ratio (SI-SDR) on a large dataset of speech corrupted by a wide variety of nonstationary noise across a range of input SNRs.
View details
Fixing a Broken ELBO
Alex Alemi
Ben Poole
Ian Fischer
Josh Dillon
Proceedings of the 35th International Conference on Machine Learning, PMLR, Stockholmsmässan, Stockholm Sweden (2018), pp. 159-168
Preview abstract
Recent work in unsupervised representation learning has focused on learning deep directed latent variable models. Fitting these models by maximizing the marginal likelihood or evidence is typically intractable, thus a common approximation is to maximize the evidence lower bound (ELBO) instead. However, maximum likelihood training (whether exact or approximate) does not necessarily result in a good latent representation, as we demonstrate both theoretically and empirically. In particular, we derive variational lower and upper bounds on the mutual information between the input and the latent variable, and use these bounds to derive a rate-distortion curve that characterizes the tradeoff between compression and reconstruction accuracy. Using this framework, we demonstrate that there is a family of models with identical ELBO, but different quantitative and qualitative characteristics. Our framework also suggests a simple new method to ensure that latent variable models with powerful stochastic decoders do not ignore their latent code.
View details
Preview abstract
Inspired by recent work on neural network image generation which
rely on backpropagation towards the network inputs, we present
a proof-of-concept system for speech texture synthesis and voice
conversion based on two mechanisms: approximate inversion of
the representation learned by a speech recognition neural network,
and on matching statistics of neuron activations between different
source and target utterances. Similar to image texture synthesis and
neural style transfer, the system works by optimizing a cost function
with respect to the input waveform samples. To this end we use a
differentiable mel-filterbank feature extraction pipeline and train a
convolutional CTC speech recognition network. Our system is able
to extract speaker characteristics from very limited amounts of target
speaker data, as little as a few seconds, and can be used to generate
realistic speech babble or reconstruct an utterance in a different voice.
View details
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Yuxuan Wang
Yu Zhang
Joel Shor
Ying Xiao
Fei Ren
ICML (2018)
Preview abstract
In this work, we propose “global style tokens”(GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system. The embeddings are trained in a completely unsupervised manner, and yet learn to model a large range of acoustic expressiveness. GSTs lead to a rich set of surprising results. The soft interpretable “labels” they generate can be used to control synthesis in novel ways, such as varying speed and modifying speak-ing style – independently of the text content. The labels can also be used for style transfer, replicating the speaking style of one “seed” phrase across an entire long-form text corpus. Perhaps most surprisingly, when trained on noisy, unlabelled found data, GSTs learn to factorize noise and speaker identity, providing a path towards highly scaleable but robust speech synthesis.
View details
