user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Random Splines for Point Process Intensity Estimation

Paper content:
Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to certain shape constraints (such as nonnegativity).
Here we propose Deep Random Splines, a flexible class of random functions obtained by transforming Gaussian noise through a deep neural network whose output are the parameters of a spline.
Unlike Gaussian processes, Deep Random Splines allow us to readily enforce shape constraints while inheriting the richness and tractability of deep generative models.
We also present an observational model for point process data which uses Deep Random Splines to model the intensity function of each point process and apply it to neuroscience data to obtain a low-dimensional representation of spiking activity.
Inference is performed via a variational autoencoder that uses a novel recurrent encoder architecture that can handle multiple point processes as input.
Gaussian Processes (GPs) are one of the main tools for modeling random functions BID24 .
They allow control of the smoothness of the function by choosing an appropriate kernel but have the disadvantage that, except in special cases (for example BID11 ; BID9 ), inference in GP models scales poorly in both memory and runtime.
Furthermore, GPs cannot easily handle shape constraints.
It can often be of interest to model a function under some shape constraint, for example nonnegativity, monotonicity or convexity/concavity BID22 BID26 BID23 BID20 .
While some shape constraints can be enforced by transforming the GP or by enforcing them at a finite number of points, doing so cannot always be done and usually makes inference harder, see for example BID18 .Splines
are another popular tool for modeling unknown functions BID29 . When there
are no shape constraints, frequentist inference is straightforward and can be performed using linear regression, by writing the spline as a linear combination of basis functions. Under shape
constraints, the basis function expansion usually no longer applies, since the space of shape constrained splines is not typically a vector space. However, the
problem can usually still be written down as a tractable constrained optimization problem BID26 . Furthermore,
when using splines to model a random function, a distribution must be placed on the spline's parameters, so the inference problem becomes Bayesian. BID7 proposed
a method to perform Bayesian inference in a setting without shape constraints, but the method relies on the basis function expansion and cannot be used in a shape constrained setting. Furthermore,
fairly simple distributions have to be placed on the spline parameters for their approximate posterior sampling algorithm to work adequately, which results in the splines having a restrictive and oversimplified distribution.On the other hand, deep probabilistic models take advantage of the major progress in neural networks to fit rich, complex distributions to data in a tractable way BID25 BID21 BID15 BID10 BID14 . However, their
goal is not usually to model random functions.In this paper, we introduce Deep Random Splines (DRS), an alternative to GPs for modeling random functions. DRS are a deep
probabilistic model in which standard Gaussian noise is transformed through a neural network to obtain the parameters of a spline, and the random function is then the corresponding spline. This combines
the complexity of deep generative models and the ability to enforce shape constraints of splines.We use DRS to model the nonnegative intensity functions of Poisson processes BID16 . In order to ensure
that the splines are nonnegative, we use a parametrization of nonnegative splines that can be written as an intersection of convex sets, and then use the method of alternating projections BID28 to obtain a point in that intersection (and differentiate through that during learning). To perform scalable
inference, we use a variational autoencoder BID15 with a novel encoder architecture that takes multiple, truly continuous point processes as input (not discretized in bins, as is common).Our contributions are
: (i) Introducing DRS,
(ii) using the method
of alternating projections to constrain splines, (iii) proposing a variational
autoencoder model whith a novel encoder architecture for point process data which uses DRS, and (iv) showing that our model outperforms
commonly used alternatives in both simulated and real data.The rest of the paper is organized as follows: we first explain DRS, how to parametrize them and how constraints can be enforced in section 2. We then present our model and how to
do inference in section 3. We then compare our model against competing
alternatives in simulated data and in two real spiking activity datasets in section 4, and observe that our method outperforms the alternatives. Finally, we summarize our work in section 5.
In this paper we introduced Deep Random Splines, an alternative to Gaussian processes to model random functions.
Owing to our key modeling choices and use of results from the spline and optimization literatures, fitting DRS is tractable and allows one to enforce shape constraints on the random functions.
While we only enforced nonnegativity and smoothness in this paper, it is straightforward to enforce constraints such as monotonicity (or convexity/concavity).
We also proposed a variational autoencoder that takes advantage of DRS to accurately model and produce meaningful low-dimensional representations of neural activity.Future work includes using DRS-VAE for multi-dimensional point processes, for example spatial point processes.
While splines would become harder to use in such a setting, they could be replaced by any family of easily-integrable nonnegative functions, such as, for example, conic combinations of Gaussian kernels.
Another line of future work involves using a more complicated point process than the Poisson, for example a Hawkes process, by allowing the parameters of the spline in a certain interval to depend on the previous spiking history of previous intervals.
Finally, DRS can be applied in more general settings than the one explored in this paper since they can be used in any setting where a random function is involved, having many potential applications beyond what we analyzed here.

assistant: text: We combine splines with neural networks to obtain a novel distribution over functions and use it to model intensity functions of point processes.