A deep learning autoencoder improves error correction for one-bit quantization.
problem Improving error correction for one-bit quantization in AWGN channels.
method Proposes a novel autoencoder-based coding scheme using turbo codes as implicit regularization.
result Empirically and theoretically shows nearly optimal performance of the proposed coding scheme.
Turbo-Aggregate reduces secure aggregation time from quadratic to nearly linear.
problem Quadratic overhead in secure model aggregation for federated learning.
method Multi-group circular strategy, additive secret sharing, and coding techniques.
result Achieves O(NlogN) overhead, compared to O(N2), for up to 50% user dropout. AdaScale-TuRBO improves high-dimensional Bayesian optimization by dynamically scaling the GP lengthscale.
problem Inappropriate lengthscale design in TuRBO's local GP model causes suboptimal performance in high dimensions.
method Proposes AdaScale-TuRBO, which scales the GP lengthscale with both problem dimension and trust region size.
result AdaScale-TuRBO robustly outperforms standard TuRBO and other methods on synthetic and real-world tasks.
Turbo-DDCM speeds up zero-shot image compression.
problem Slowness and high computational demand in zero-shot diffusion-based compression.
method Modified DDCM framework with Turbo-DDCM, combining noise vectors and improved encoding.
result Turbo-DDCM achieves faster compression than state-of-the-art methods.
Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the disco…
Turbo-Sim generates models from physics principles, improving interpretability and flexibility.
problem Transforming particle properties from theory to observation in collider physics.
method Maximizes mutual information between input and output, setting loss term weights.
result Mathematically interpretable and flexible generative models.
A new method reduces the time needed for Bayesian optimization by a factor of 10-100.
problem Efficiently scaling Bayesian optimization to many observations.
method Epistemic Nearest Neighbors (ENN) for hyperparameter fitting and UCB acquisition.
result TuRBO-ENN reduces proposal time by one to two orders of magnitude compared to TuRBO.
FinanceBench benchmarks LLMs on financial QA, revealing limitations.
problem Evaluating LLMs' performance on financial question answering.
method Developed a comprehensive test suite (FinanceBench) with 10,231 questions, tested 16 models, and manually reviewed answers.
result Existing LLMs have significant limitations for financial QA, especially GPT-4-Turbo.
This paper evaluates LLMs for technical market analysis, finding GPT-4 Turbo and FinGPT outperform passive benchmarks.
problem Evaluating LLMs for technical market analysis in financial markets.
method Structured evaluation of five LLMs (GPT-4 Turbo, Claude 3 Opus, Gemini 1.5 Pro, Llama 3 70B, FinGPT) on four tasks: candlestick pattern recognition, directional signal generation, backtesting, and financial report comprehension.
result GPT-4 Turbo and FinGPT outperform passive benchmarks in simulated backtesting, with GPT-4 Turbo achieving the highest annualized return and Sharpe ratio.
A new approach for blind channel equalization and decoding, variational inference, and variational autoencoders (VAEs) in particular, is introduced. We first consider the reconstruction of uncoded data symbols transmitted over a noisy linear intersymbol interference (ISI) channel, with an unknown impulse response, with…
Bayesian optimization improved for large-scale problems.
problem Efficient optimization of expensive functions with many variables.
method Local Bayesian optimization using a collection of local models and a bandit approach for sample allocation.
result TuRBO algorithm outperforms state-of-the-art methods on various high-dimensional problems.
Enhances BO in high dimensions with Newton methods.
problem Challenges in scaling BO to high-dimensional spaces.
method Construct multiple local quadratic models using gradients and Hessians from a global GP, and select new sample points by solving bound-constrained quadratic programs.
result Outperforms existing high-dimensional BO techniques on synthetic and real-world applications.
This paper presents a Bayesian approach to symbol and phase inference in a phase-unsynchronized digital receiver. It primarily extends [Quinn 2011] to the multi-symbol case, using the variational Bayes (VB) approximation to deal with the combinatorial complexity of the phase inference in this case. The work provides a …
SPPO optimizes language model alignment by treating preferences as a game and achieving state-of-the-art performance.
problem Capturing intransitivity and irrationality in human preferences for accurate language model alignment.
method Self-play-based approach to identify Nash equilibrium policy through iterative policy updates.
result SPPO achieves state-of-the-art win-rate of 28.53% on AlpacaEval 2.0 without external supervision.
LLMs can help explain credit risk models but not autonomously.
problem Leveraging LLMs for post-hoc explainability in credit risk models.
method Comparison of LLM outputs with SHAP and coefficient-based attributions on three LMs.
result LLMs reliably preserve feature-importance rankings but poorly align with autonomous explanations.
We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments. Impulsive noise arises in many modern wireless and wireline communication systems, such as Wi-Fi and powerline communications, due to uncoordinated interference that is much stronger than the…
Optimizes data acquisition in high-dimensional Bayesian optimization.
problem Suboptimal data acquisition in high-dimensional Bayesian optimization tasks.
method Utility-calibrated variational inference to align approximations with BO goals.
result Optimal data acquisition decisions under a limited computational budget.
Automatically jailbreaks LLMs with black-box access.
problem Generating harmful content from black-box LLMs.
method Automated method using an attacker LLM to refine prompts.
result Generates jailbreaks for over 80% of prompts.
CoMPNetX uses neural networks to efficiently solve constrained motion planning problems.
problem Finding collision-free paths on constraint manifolds efficiently.
method Neural generator and discriminator with neural gradients-based projection operator.
result CoMPNetX finds path solutions with high success rates and lower computation times.
The paper proposes a framework to calibrate multi-agent simulation models from output series using Bayesian optimization.
problem Calibrating multi-agent simulation models from observable output series.
method Novel eligibility set concept, two-sample Kolmogorov-Smirnov test with Bonferroni correction, Bayesian optimization (BO), and trust-region BO (TuRBO).
result Demonstrated the efficiency of the proposed framework using numerical experiments.
Many software analysis methods have come to rely on machine learning approaches. Code segmentation - the process of decomposing source code into meaningful blocks - can augment these methods by featurizing code, reducing noise, and limiting the problem space. Traditionally, code segmentation has been done using syntact…
Novel method for high-dimensional BO using CMA to define local regions.
problem Challenges in applying BO to high-dimensional optimization problems.
method CMA strategy to learn search distribution and define local regions.
result Our method outperforms existing techniques on various benchmarks.
ECLIPSE detects AI hallucinations in finance with high accuracy.
problem Hallucinations in AI-generated answers limit safe deployment in finance.
method Combines entropy estimation and perplexity decomposition to measure model evidence use.
result ECLIPSE achieves ROC AUC of 0.89 and average precision of 0.90 on financial QA dataset.
TreeCaps improves code comprehension for software developers.
problem Processing code efficiently for software developers.
method Tree-based capsule networks for capturing code syntactical structures and dependencies.
result TreeCaps outperforms other approaches in classifying program functionalities.
Study re-evaluates MIMIC-III codes, finding many are under-coded.
problem Validity of MIMIC-III clinical codes is questionable.
method Open-source, reproducible methodology for assessing codes.
result Most frequently assigned codes are under-coded up to 35%
Paper proposes new gradient codes for robust distributed machine learning.
problem Robustness against slow machines (stragglers) in distributed machine learning.
method Constructs gradient codes based on probabilistic and Kronecker product methods.
result Gradient codes exist for a wide range of system parameters with comparable error performance.
New quantum codes improve error correction with local tests.
problem Improving quantum error correction efficiency.
method Introducing hemicubic codes and exploiting their local testability.
result Quantum codes with improved local testability and error correction.
CoNCRA uses CNN to find code snippets matching developer intent.
problem Finding relevant code snippets using general search engines.
method Convolutional Neural Network approach to code retrieval.
result Improved code retrieval by 5% on average, top 3 positions 80% of the time.
Paper proposes graph-based separable transforms for video coding.
problem Improving video coding efficiency by better capturing residual block statistics.
method Derives graph-based separable transforms (GBSTs) from line graphs with weights determined by parameters.
result GBSTs achieve about 0.4% average coding gain over existing transforms in VVC.
Adversarial attacks found to be effective on code models.
problem Adversarial robustness of code models.
method Instantiated adversarial attacks for code, demonstrated vulnerability, and improved robustness.
result Neural models for code are vulnerable to adversarial attacks.
Contrastive Code Representation Learning improves code summarization and type inference.
problem Code representations are sensitive to edits, hindering downstream semantic understanding tasks.
method ContraCode: a contrastive pre-training task that learns code functionality.
result Contrastive pre-training improves code summarization and type inference accuracy.
Bayesian optimization improved for high-dimensional outputs using randomized priors.
problem Efficient global optimization of high-dimensional black-box functions.
method Deep learning framework with bootstrapped ensembles of neural architectures with randomized priors.
result Superior performance in tasks with high-dimensional outputs compared to state-of-the-art methods.
This work tackles runtime complexity prediction for code, using machine learning and a new dataset.
problem Predicting runtime complexity of code is hard and mathematically impossible.
method Modelled as a machine learning task, using feature engineering and code embeddings, with a new dataset.
result Achieved state-of-the-art results in runtime complexity prediction.
Transformer model improves source code summarization.
problem Generating readable summaries of source code.
method Transformer model with self-attention mechanism for code representation.
result Transformer model outperforms state-of-the-art techniques.
SLM models code syntax as trees to generate any programming language code.
problem Generating any piece of code in a given language without restrictions.
method Structural language modeling (SLM) decomposes code into ASTs and estimates probabilities over nodes.
result SLM model generates arbitrary code in any language, outperforming previous methods.
Investigates neural codes and their embeddings, proving conjectures and introducing new code types.
problem Analyzing neural codes and their embedding dimensions.
method Combinatorial, topological, and algebraic analysis; proving conjectures; introducing new neural code types.
result Proves conjectures about neural codes and their embeddings, introduces new code types.
Characterization of a patient clinical phenotype is central to biomedical informatics. ICD codes, assigned to inpatient encounters by coders, is important for population health and cohort discovery when clinical information is limited. While ICD codes are assigned to patients by professionals trained and certified in c…
Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the m…
Inspired by recent work on convex formulations of clustering (Lashkari & Golland, 2008; Nowozin & Bakir, 2008) we investigate a new formulation of the Sparse Coding Problem (Olshausen & Field, 1997). In sparse coding we attempt to simultaneously represent a sequence of data-vectors sparsely (i.e. sparse approximation (…
Topological theory for qLDPC codes enables non-Clifford gates and magic state injection.
problem Fault-tolerant quantum computation in qLDPC codes with non-Clifford gates and magic state resources.
method Developed a topological theory using simplicial or CW complex structures and deformation retraction.
result Achieved non-Clifford gates and magic state injection in qLDPC codes with constant rate and polynomial distance.
We introduce new definitions of universal and superuniversal computable codes, which are based on a code's ability to approximate Kolmogorov complexity within the prescribed margin for all individual sequences from a given set. Such sets of sequences may be singled out almost surely with respect to certain probability …
Code search and comprehension have become more difficult in recent years due to the rapid expansion of available source code. Current tools lack a way to label arbitrary code at scale while maintaining up-to-date representations of new programming languages, libraries, and functionalities. Comprehensive labeling of sou…
Gradient coding is a technique for straggler mitigation in distributed learning. In this paper we design novel gradient codes using tools from classical coding theory, namely, cyclic MDS codes, which compare favorably with existing solutions, both in the applicable range of parameters and in the complexity of the invol…
The design of codes for communicating reliably over a statistically well defined channel is an important endeavor involving deep mathematical research and wide-ranging practical applications. In this work, we present the first family of codes obtained via deep learning, which significantly beats state-of-the-art codes …
MISIM improves code similarity systems with neural learning.
problem Improving accuracy of code semantics similarity systems.
method Context-aware semantics structure and neural code similarity scoring algorithm.
result MISIM achieves 8.08% better accuracy than state-of-the-art systems.
Khovanov homology helps create quantum error-correcting codes.
problem Creating robust quantum error-correcting codes.
method Using Khovanov homology and its extensions to define and analyze quantum codes.
result New families of quantum codes with desirable properties.
Sparse codes improve optimal control tasks with correlated inputs.
problem Optimal control tasks with correlated feature inputs.
method Used a sparse code to represent natural images in an optimal control task solved with neuro-dynamic programming.
result An over-complete sparse code increases memory capacity and learning speed beyond a complete code.
We discuss Gauss codes of virtual diagrams and virtual doodles. The notion of a left canonical Gauss code is introduced and it is shown that oriented virtual doodles are uniquely presented by left canonical Gauss codes.