Single-cell transcriptomics has revolutionized our understanding of cellular heterogeneity and drug perturbation effects. However, its high cost and the vast chemical space of potential drugs present barriers to experimentally characterizing the effect of chemical perturbations in all the myriad cell types of the human body. To overcome these limitations, several groups have proposed using machine learning methods to directly predict the effect of chemical perturbations either across cell contexts or chemical space. However, advances in this field have been hindered by a lack of well-designed evaluation datasets and benchmarks. To drive innovation in perturbation modeling, the Open Problems Perturbation Prediction (OP3) benchmark introduces a framework for predicting the effects of small molecule perturbations on cell type-specific gene expression. OP3 leverages the Open Problems in Single-cell Analysis benchmarking infrastructure and is enabled by a new single-cell perturbation dataset, encompassing 146 compounds tested on human blood cells. The benchmark includes diverse data representations, evaluation metrics, and winning methods from our "Single-cell perturbation prediction: generalizing experimental interventions to unseen contexts" competition at NeurIPS 2023. We envision that the OP3 benchmark and competition will drive innovation in single-cell perturbation prediction by improving the accessibility, visibility, and feasibility of this challenge, thereby promoting the impact of machine learning in drug discovery.

Early work on narrative modeling used explicit plans and goals to generate stories, but the language generation itself was restricted and inflexible.Modern methods use language models for more robust generation, but often lack an explicit representation of the scaffolding and dynamics that guide a coherent narrative.This paper introduces a new model that integrates explicit narrative structure with neural language models, formalizing narrative modeling as a Switching Linear Dynamical System (SLDS).A SLDS is a dynamical system in which the latent dynamics of the system (i.e.how the state vector transforms over time) is controlled by top-level discrete switching variables.The switching variables represent narrative structure (e.g., sentiment or discourse states), while the latent state vector encodes information on the current state of the narrative.This probabilistic formulation allows us to control generation, and can be learned in a semi-supervised fashion using both labeled and unlabeled data.Additionally, we derive a Gibbs sampler for our model that can "fill in" arbitrary parts of the narrative, guided by the switching variables.Our filled-in (English language) narratives outperform several baselines on both automatic and human evaluations.

When does a sequence of events define an everyday scenario and how can this knowledge be induced from text?Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus.We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions.Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents.

Today there is no effective support for device-wide question answering on mobile devices. State-of-the-art QA models are deep learning behemoths designed for the cloud which run extremely slow and require more memory than available on phones. We present DeQA, a suite of latency- and memory- optimizations that adapts existing QA systems to run completely locally on mobile phones. Specifically, we design two latency optimizations that (1) stops processing documents if further processing cannot improve answer quality, and (2) identifies computation that does not depend on the question and moves it offline. These optimizations do not depend on the QA model internals and can be applied to several existing QA models. DeQA also implements a set of memory optimizations by (i) loading partial indexes in memory, (ii) working with smaller units of data, and (iii) replacing in-memory lookups with a key-value database. We use DeQA to port three state-of-the-art QA systems to the mobile device and evaluate over three datasets. The first is a large scale SQuAD dataset defined over Wikipedia collection. We also create two on-device QA datasets, one over a publicly available email data collection and the other using a cross-app data collection we obtain from two users. Our evaluations show that DeQA can run QA models with only a few hundred MBs of memory and provides at least 13x speedup on average on the mobile phone across all three datasets.% with less than a 1% drop in accuracy.

Robust and flexible event representations are important to many core areas in language understanding. Scripts were proposed early on as a way of representing sequences of events for such understanding, and has recently attracted renewed attention. However, obtaining effective representations for modeling script-like event sequences is challenging. It requires representations that can capture event-level and scenario-level semantics. We propose a new tensor-based composition method for creating event representations. The method captures more subtle semantic interactions between an event and its entities and yields representations that are effective at multiple event-related tasks. With the continuous representations, we also devise a simple schema generation method which produces better schemas compared to a prior discrete representation based method. Our analysis shows that the tensors capture distinct usages of a predicate even when there are only subtle differences in their surface realizations.

Scripts define knowledge about how everyday scenarios (such as going to a restaurant) are expected to unfold.One of the challenges to learning scripts is the hierarchical nature of the knowledge.For example, a suspect arrested might plead innocent or guilty, and a very different track of events is then expected to happen.To capture this type of information, we propose an autoencoder model with a latent space defined by a hierarchy of categorical variables.We utilize a recently proposed vector quantization based approach, which allows continuous embeddings to be associated with each latent variable value.This permits the decoder to softly decide what portions of the latent hierarchy to condition on by attending over the value embeddings for a given setting.Our model effectively encodes and generates scripts, outperforming a recent language modeling-based method on several standard tasks, and allowing the autoencoder model to achieve substantially lower perplexity scores compared to the previous language modelingbased method.

Quartet trees displayed by larger phylogenetic trees have long been used as inputs for species tree and supertree reconstruction. Computational constraints prevent the use of all displayed quartets in many practical problems with large numbers of taxa. We introduce the notion of an Efficient Quartet System (EQS) to represent a phylogenetic tree with a subset of the quartets displayed by the tree. We show mathematically that the set of quartets obtained from a tree via an EQS contains all of the combinatorial information of the tree itself. Using performance tests on simulated datasets, we also demonstrate that using an EQS to reduce the number of quartets in both summary method pipelines for species tree inference as well as methods for supertree inference results in only small reductions in accuracy.