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Found 18 papers, 8 papers with code
Learning DAGs and Root Causes from Time-Series Data
We introduce DAG-TFRC, a novel method for learning directed acyclic graphs (DAGs) from time series with few root causes.
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data
In drug discovery, mapping interactions between genes within cellular systems is a crucial early step.
QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models
We present ongoing work on a new automatic code generation approach for supporting quantized generative inference on LLMs such as LLaMA or OPT on off-the-shelf CPUs.
Learning DAGs from Data with Few Root Causes
We prove identifiability in this new setting and show that the true DAG is the global minimizer of the $L^0$-norm of the vector of root causes.
Causal Fourier Analysis on Directed Acyclic Graphs and Posets
Specifically, we model the spread of an infection on such a DAG obtained from real-world contact tracing data and learn the infection signal from samples assuming sparsity in the Fourier domain.
PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations
Formal verification of neural networks is critical for their safe adoption in real-world applications.
Faster Training of Word Embeddings
Word embeddings have gained increasing popularity in the recent years due to the Word2vec library and its extension fastText that uses subword information.
Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases
Many applications of machine learning on discrete domains, such as learning preference functions in recommender systems or auctions, can be reduced to estimating a set function that is sparse in the Fourier domain.
Scaling Polyhedral Neural Network Verification on GPUs
GPUPoly scales to large networks: for example, it can prove the robustness of a 1M neuron, 34-layer deep residual network in approximately 34. 5 ms. We believe GPUPoly is a promising step towards practical verification of real-world neural networks.
Digraph Signal Processing with Generalized Boundary Conditions
Furthermore, the Fourier transform in this case is now obtained from the Jordan decomposition, which may not be computable at all for large graphs.
Discrete Signal Processing with Set Functions
Set functions are functions (or signals) indexed by the powerset (set of all subsets) of a finite set N. They are fundamental and ubiquitous in many application domains and have been used, for example, to formally describe or quantify loss functions for semantic image segmentation, the informativeness of sensors in sensor networks the utility of sets of items in recommender systems, cooperative games in game theory, or bidders in combinatorial auctions.
Beyond the Single Neuron Convex Barrier for Neural Network Certification
We propose a new parametric framework, called k-ReLU, for computing precise and scalable convex relaxations used to certify neural networks.
Powerset Convolutional Neural Networks
We present a novel class of convolutional neural networks (CNNs) for set functions, i. e., data indexed with the powerset of a finite set.
On Linear Learning with Manycore Processors
A new generation of manycore processors is on the rise that offers dozens and more cores on a chip and, in a sense, fuses host processor and accelerator.
Robustness Certification with Refinement
We present a novel approach for verification of neural networks which combines scalable over-approximation methods with precise (mixed integer) linear programming.
Fast and Effective Robustness Certification
We present a new method and system, called DeepZ, for certifying neural network robustness based on abstract interpretation.
Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications
Modern scientific instruments produce vast amounts of data, which can overwhelm the processing ability of computer systems.
Distributed Optimization With Local Domains: Applications in MPC and Network Flows
Our contribution is a communication-efficient distributed algorithm that finds a vector $x^\star$ minimizing the sum of all the functions.
Optimization and Control Information Theory Information Theory
