@conference{seff2019discrete,

title = {Discrete Object Generation with Reversible Inductive Construction},

author = {Ari Seff and

Wenda Zhou and

Farhan Damani and

Abigail Doyle and

Ryan P. Adams},

url = {https://www.cs.princeton.edu/~rpa/pubs/seff2019discrete.pdf},

year  = {2019},

date = {2019-12-04},

booktitle = {Advances in Neural Information Processing Systems 32 (NeurIPS)},

abstract = {The success of generative modeling in continuous domains has led to a surge of interest in generating discrete data such as molecules, source code, and graphs. However, construction histories for these discrete objects are typically not unique and so generative models must reason about intractably large spaces in order to learn. Additionally, structured discrete domains are often characterized by strict constraints on what constitutes a valid object and generative models must respect these requirements in order to produce useful novel samples. Here, we present a generative model for discrete objects employing a Markov chain where transitions are restricted to a set of local operations that preserve validity. Building off of generative interpretations of denoising autoencoders, the Markov chain alternates between producing 1) a sequence of corrupted objects that are valid but not from the data distribution, and 2) a learned reconstruction distribution that attempts to fix the corruptions while also preserving validity. This approach constrains the generative model to only produce valid objects, requires the learner to only discover local modifications to the objects, and avoids marginalization over an unknown and potentially large space of construction histories. We evaluate the proposed approach on two highly structured discrete domains, molecules and Laman graphs, and find that it compares favorably to alternative methods at capturing distributional statistics for a host of semantically relevant metrics.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{linderman2016multi,

title = {Bayesian Latent Structure Discovery from Multi-neuron Recordings},

author = {Scott W. Linderman and Ryan P. Adams and Jonathan W. Pillow},

url = {http://www.cs.princeton.edu/~rpa/pubs/linderman2016multi.pdf},

year  = {2016},

date = {2016-01-01},

booktitle = {Advances in Neural Information Processing Systems (NIPS) 29},

abstract = {Neural circuits contain heterogeneous groups of neurons that 

 differ in type, location, connectivity, and basic response 

 properties. However, traditional methods for dimensionality 

 reduction and clustering are ill-suited to recovering the 

 structure underlying the organization of neural circuits. In 

 particular, they do not take advantage of the rich temporal 

 dependencies in multi-neuron recordings and fail to account 

 for the noise in neural spike trains. Here we describe new 

 tools for inferring latent structure from simultaneously 

 recorded spike train data using a hierarchical extension of a 

 multi-neuron point process model commonly known as the 

 generalized linear model (GLM). Our approach combines the GLM 

 with flexible graph-theoretic priors governing the 

 relationship between latent features and neural connectivity 

 patterns. Fully Bayesian inference via Pólya-gamma 

 augmentation of the resulting model allows us to classify 

 neurons and infer latent dimensions of circuit organization 

 from correlated spike trains. We demonstrate the effectiveness 

 of our method with applications to synthetic data and 

 multi-neuron recordings in primate retina, revealing latent 

 patterns of neural types and locations from spike trains 

 alone.},

note = {arXiv:1610.08465 [stat.ML]},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{duvenaud2015fingerprints,

title = {Convolutional Networks on Graphs for Learning Molecular Fingerprints},

author = {David Duvenaud and Dougal Maclaurin and Jorge Aguilera-Iparraguirre and Rafael Gómez-Bombarelli and Timothy D. Hirzel and Alan Aspuru-Guzik and Ryan P. Adams},

url = {http://www.cs.princeton.edu/~rpa/pubs/duvenaud2015fingerprints.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Advances in Neural Information Processing Systems (NIPS) 28},

abstract = {We introduce a convolutional neural network that operates 

 directly on graphs. These networks allow end-to-end learning 

 of prediction pipelines whose inputs are graphs of arbitrary 

 size and shape. The architecture we present generalizes 

 standard molecular feature extraction methods based on 

 circular fingerprints. We show that these data-driven features 

 are more interpretable, and have better predictive performance 

 on a variety of tasks.},

note = {arXiv:1509.09292 [stat.ML]},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{regier2015celeste,

title = {Celeste: Variational Inference for a Generative Model of Astronomical Images},

author = {Jeffrey Regier and Andrew C. Miller and Jon McAuliffe and Ryan P. Adams and Matthew D. Hoffman and Dustin Lang and David Schlegel and Prabhat},

url = {http://www.cs.princeton.edu/~rpa/pubs/regier2015celeste.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the 32nd International Conference on Machine Learning (ICML)},

abstract = {We present a new, fully generative model of optical telescope 

 image sets, along with a variational procedure for 

 inference. Each pixel intensity is treated as a Poisson random 

 variable, with a rate parameter dependent on latent properties 

 of stars and galaxies. Key latent properties are themselves 

 random, with scientific prior distributions constructed from 

 large ancillary data sets. We check our approach on synthetic 

 images. We also run it on images from a major sky survey, 

 where it exceeds the performance of the current 

 state-of-the-art method for locating celestial bodies and 

 measuring their colors.},

note = {arXiv:1506.01351 [astro-ph.IM]},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{miller2015quasars,

title = {A Gaussian Process Model of Quasar Spectral Energy Distributions},

author = {Andrew C. Miller and Albert Wu and Jeffrey Regier and Jon McAuliffe and Dustin Lang and Prabhat and David Schlegel and Ryan P. Adams},

url = {http://www.cs.princeton.edu/~rpa/pubs/miller2015quasars.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Advances in Neural Information Processing Systems (NIPS) 28},

abstract = {We propose a method for combining two sources of astronomical 

 data, spectroscopy and photometry, that carry information 

 about sources of light (e.g., stars, galaxies, and quasars) at 

 extremely different spectral resolutions. Our model treats the 

 spectral energy distribution (SED) of the radiation from a 

 source as a latent variable that jointly explains both 

 photometric and spectroscopic observations. We place a 

 flexible, nonparametric prior over the SED of a light source 

 that admits a physically interpretable decomposition, and 

 allows us to tractably perform inference. We use our model to 

 predict the distribution of the redshift of a quasar from 

 five-band (low spectral resolution) photometric data, the so 

 called photo-z'' problem. Our method shows that tools from 

 machine learning and Bayesian statistics allow us to leverage 

 multiple resolutions of information to make accurate 

 predictions with well-characterized uncertainties.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{linderman2014plasticity,

title = {A Framework for Studying Synaptic Plasticity with Neural Spike Train Data},

author = {Scott W. Linderman and Christopher H. Stock and Ryan P. Adams},

url = {http://www.cs.princeton.edu/~rpa/pubs/linderman2014plasticity.pdf},

year  = {2014},

date = {2014-01-01},

booktitle = {Advances in Neural Information Processing Systems (NIPS) 27},

abstract = {Learning and memory in the brain are implemented by complex, 

 time-varying changes in neural circuitry. The computational 

 rules according to which synaptic weights change over time are 

 the subject of much research, and are not precisely 

 understood. Until recently, limitations in experimental 

 methods have made it challenging to test hypotheses about 

 synaptic plasticity on a large scale. However, as such data 

 become available and these barriers are lifted, it becomes 

 necessary to develop analysis techniques to validate 

 plasticity models. Here, we present a highly extensible 

 framework for modeling arbitrary synaptic plasticity rules on 

 spike train data in populations of interconnected neurons. We 

 treat synaptic weights as a (potentially nonlinear) dynamical 

 system embedded in a fully-Bayesian generalized linear model 

 (GLM). In addition, we provide an algorithm for inferring 

 synaptic weight trajectories alongside the parameters of the 

 GLM and of the learning rules. Using this method, we perform 

 model comparison of two proposed variants of the well-known 

 spike-timing-dependent plasticity (STDP) rule, where nonlinear 

 effects play a substantial role. On synthetic data generated 

 from the biophysical simulator NEURON, we show that we can 

 recover the weight trajectories, the pattern of connectivity, 

 and the underlying learning rules.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{napp2013reactions,

title = {Message Passing Inference with Chemical Reaction Networks},

author = {Nils Napp and Ryan P. Adams},

url = {http://www.cs.princeton.edu/~rpa/pubs/napp2013reactions.pdf},

year  = {2013},

date = {2013-01-01},

booktitle = {Advances in Neural Information Processing Systems (NIPS) 26},

abstract = {Recent work on molecular programming has explored new 

 possibilities for computational abstractions with 

 biomolecules, including logic gates, neural networks, and 

 linear systems. In the future such abstractions might enable 

 nanoscale devices that can sense and control the world at a 

 molecular scale. Just as in macroscale robotics, it is 

 critical that such devices can learn about their environment 

 and reason under uncertainty. At this small scale, systems are 

 typically modeled as chemical reaction networks. In this work, 

 we develop a procedure that can take arbitrary probabilistic 

 graphical models, represented as factor graphs over discrete 

 random variables, and compile them into chemical reaction 

 networks that implement inference. In particular, we show that 

 marginalization based on sum-product message passing can be 

 implemented in terms of reactions between chemical species 

 whose concentrations represent probabilities. We show 

 algebraically that the steady state concentration of these 

 species correspond to the marginal distributions of the random 

 variables in the graph and validate the results in 

 simulations. As with standard sum-product inference, this 

 procedure yields exact results for tree-structured graphs, and 

 approximate solutions for loopy graphs.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{snoek2013determinantal,

title = {A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data},

author = {Jasper Snoek and Ryan P. Adams and Richard S. Zemel},

url = {http://www.cs.princeton.edu/~rpa/pubs/snoek2013determinantal.pdf},

year  = {2013},

date = {2013-01-01},

booktitle = {Advances in Neural Information Processing Systems (NIPS) 26},

abstract = {Point processes are popular models of neural spiking behavior 

 as they provide a statistical distribution over temporal 

 sequences of spikes and help to reveal the complexities 

 underlying a series of recorded action potentials. However, 

 the most common neural point process models, the Poisson 

 process and the gamma renewal process, do not capture 

 interactions and correlations that are critical to modeling 

 populations of neurons. We develop a novel model based on a 

 determinantal point process over latent embeddings of neurons 

 that effectively captures and helps visualize complex 

 inhibitory and competitive interaction. We show that this 

 model is a natural extension of the popular generalized linear 

 model to sets of interacting neurons. The model is extended to 

 incorporate gain control or divisive normalization, and the 

 modulation of neural spiking based on periodic 

 phenomena. Applied to neural spike recordings from the rat 

 hippocampus, we see that the model captures inhibitory 

 relationships, a dichotomy of classes of neurons, and a 

 periodic modulation by the theta rhythm known to be present in 

 the data.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}