pwc-archive/papers-with-abstracts huggingface dataset:
Improving ASR Contextual Biasing with Guided Attention
Authors: Jiyang Tang, Kwangyoun Kim, Suwon Shon, Felix Wu, Prashant Sridhar, Shinji Watanabe
Date: 2024-01-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In this paper, we propose a Guided Attention (GA) auxiliary training loss, which improves the effectiveness and robustness of automatic speech recognition (ASR) contextual biasing without introducing additional parameters. A common challenge in previous literature is that the word error rate (WER) reduction brought by contextual biasing diminishes as the number of bias phrases increases. To address this challenge, we employ a GA loss as an additional training objective besides the Transducer loss. The proposed GA loss aims to teach the cross attention how to align bias phrases with text tokens or audio frames. Compared to studies with similar motivations, the proposed loss operates directly on the cross attention weights and is easier to implement. Through extensive experiments based on Conformer Transducer with Contextual Adapter, we demonstrate that the proposed method not only leads to a lower WER but also retains its effectiveness as the number of bias phrases increases. Specifically, the GA loss decreases the WER of rare vocabularies by up to 19.2% on LibriSpeech compared to the contextual biasing baseline, and up to 49.3% compared to a vanilla Transducer.
Revisiting Self-supervised Learning of Speech Representation from a Mutual Information Perspective
Authors: Alexander H. Liu, Sung-Lin Yeh, James Glass
Date: 2024-01-16 00:00:00
Tasks: Representation Learning, Self-Supervised Learning, speech-recognition, Speech Recognition, Speech Representation Learning

Existing studies on self-supervised speech representation learning have focused on developing new training methods and applying pre-trained models for different applications. However, the quality of these models is often measured by the performance of different downstream tasks. How well the representations access the information of interest is less studied. In this work, we take a closer look into existing self-supervised methods of speech from an information-theoretic perspective. We aim to develop metrics using mutual information to help practical problems such as model design and selection. We use linear probes to estimate the mutual information between the target information and learned representations, showing another insight into the accessibility to the target information from speech representations. Further, we explore the potential of evaluating representations in a self-supervised fashion, where we estimate the mutual information between different parts of the data without using any labels. Finally, we show that both supervised and unsupervised measures echo the performance of the models on layer-wise linear probing and speech recognition.
HuixiangDou: Overcoming Group Chat Scenarios with LLM-based Technical Assistance
Authors: Huanjun Kong, Songyang Zhang, Jiaying Li, Min Xiao, Jun Xu, Kai Chen
Date: 2024-01-16 00:00:00
Tasks: In-Context Learning

In this work, we present HuixiangDou, a technical assistant powered by Large Language Models (LLM). This system is designed to assist algorithm developers by providing insightful responses to questions related to open-source algorithm projects, such as computer vision and deep learning projects from OpenMMLab. We further explore the integration of this assistant into the group chats of instant messaging (IM) tools such as WeChat and Lark. Through several iterative improvements and trials, we have developed a sophisticated technical chat assistant capable of effectively answering users' technical questions without causing message flooding. This paper's contributions include: 1) Designing an algorithm pipeline specifically for group chat scenarios; 2) Verifying the reliable performance of text2vec in task rejection; 3) Identifying three critical requirements for LLMs in technical-assistant-like products, namely scoring ability, In-Context Learning (ICL), and Long Context. We have made the source code, android app and web service available at Github (https://github.com/internlm/huixiangdou), OpenXLab (https://openxlab.org.cn/apps/detail/tpoisonooo/huixiangdou-web) and YouTube (https://youtu.be/ylXrT-Tei-Y) to aid in future research and application. HuixiangDou is applicable to any group chat within IM tools.
Information flow and Laplacian dynamics on local optima networks
Authors: Hendrik Richter, Sarah L. Thomson
Date: 2024-01-17 00:00:00

We propose a new way of looking at local optima networks (LONs). LONs represent fitness landscapes; the nodes are local optima, and the edges are search transitions between them. Many metrics computed on LONs have been proposed and shown to be linked to metaheuristic search difficulty. These have typically considered LONs as describing static structures. In contrast to this, Laplacian dynamics (LD) is an approach to consider the information flow across a network as a dynamical process. We adapt and apply LD to the context of LONs. As a testbed, we consider instances from the quadratic assignment problem (QAP) library. Metrics related to LD are proposed and these are compared with existing LON metrics. The results show that certain LD metrics are strong predictors of metaheuristic performance for iterated local search and tabu search.
Deep Pulse-Coupled Neural Networks
Authors: Zexiang Yi, Jing Lian, Yunliang Qi, Zhaofei Yu, Huajin Tang, Yide Ma, Jizhao Liu
Date: 2023-12-24 00:00:00

Spiking Neural Networks (SNNs) capture the information processing mechanism of the brain by taking advantage of spiking neurons, such as the Leaky Integrate-and-Fire (LIF) model neuron, which incorporates temporal dynamics and transmits information via discrete and asynchronous spikes. However, the simplified biological properties of LIF ignore the neuronal coupling and dendritic structure of real neurons, which limits the spatio-temporal dynamics of neurons and thus reduce the expressive power of the resulting SNNs. In this work, we leverage a more biologically plausible neural model with complex dynamics, i.e., a pulse-coupled neural network (PCNN), to improve the expressiveness and recognition performance of SNNs for vision tasks. The PCNN is a type of cortical model capable of emulating the complex neuronal activities in the primary visual cortex. We construct deep pulse-coupled neural networks (DPCNNs) by replacing commonly used LIF neurons in SNNs with PCNN neurons. The intra-coupling in existing PCNN models limits the coupling between neurons only within channels. To address this limitation, we propose inter-channel coupling, which allows neurons in different feature maps to interact with each other. Experimental results show that inter-channel coupling can efficiently boost performance with fewer neurons, synapses, and less training time compared to widening the networks. For instance, compared to the LIF-based SNN with wide VGG9, DPCNN with VGG9 uses only 50%, 53%, and 73% of neurons, synapses, and training time, respectively. Furthermore, we propose receptive field and time dependent batch normalization (RFTD-BN) to speed up the convergence and performance of DPCNNs.
Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning
Authors: Maxence Faldor, Félix Chalumeau, Manon Flageat, Antoine Cully
Date: 2023-12-10 00:00:00
Tasks: Continuous Control, Diversity, Evolutionary Algorithms, reinforcement-learning, Reinforcement Learning

A hallmark of intelligence is the ability to exhibit a wide range of effective behaviors. Inspired by this principle, Quality-Diversity algorithms, such as MAP-Elites, are evolutionary methods designed to generate a set of diverse and high-fitness solutions. However, as a genetic algorithm, MAP-Elites relies on random mutations, which can become inefficient in high-dimensional search spaces, thus limiting its scalability to more complex domains, such as learning to control agents directly from high-dimensional inputs. To address this limitation, advanced methods like PGA-MAP-Elites and DCG-MAP-Elites have been developed, which combine actor-critic techniques from Reinforcement Learning with MAP-Elites, significantly enhancing the performance and efficiency of Quality-Diversity algorithms in complex, high-dimensional tasks. While these methods have successfully leveraged the trained critic to guide more effective mutations, the potential of the trained actor remains underutilized in improving both the quality and diversity of the evolved population. In this work, we introduce DCRL-MAP-Elites, an extension of DCG-MAP-Elites that utilizes the descriptor-conditioned actor as a generative model to produce diverse solutions, which are then injected into the offspring batch at each generation. Additionally, we present an empirical analysis of the fitness and descriptor reproducibility of the solutions discovered by each algorithm. Finally, we present a second empirical analysis shedding light on the synergies between the different variations operators and explaining the performance improvement from PGA-MAP-Elites to DCRL-MAP-Elites.
The Conquest of Quantum Genetic Algorithms: The Adventure to Cross the Valley of Death
Authors: Rafael Lahoz-Beltra
Date: 2023-12-10 00:00:00
Tasks: Evolutionary Algorithms

In recent years, the emergence of the first quantum computers at a time when AI is undergoing a fruitful era has led many AI researchers to be tempted into adapting their algorithms to run on a quantum computer. However, in many cases the initial enthusiasm has ended in frustration, since the features and principles underlying quantum computing are very different from traditional computers. In this paper, we present a discussion of the difficulties arising when designing a quantum version of an evolutionary algorithm based on Darwin's evolutionary mechanism, the so-called genetic algorithms. The paper includes the code in both Python and QISKIT of the quantum version of one of these evolutionary algorithms allowing the reader to experience the setbacks arising when translating a classical algorithm to its quantum version. The algorithm studied in this paper, termed RQGA (Reduced Quantum Genetic Algorithm), has been chosen as an example that clearly shows these difficulties, which are common to other AI algorithms.
Wake-Sleep Consolidated Learning
Authors: Amelia Sorrenti, Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi, Simone Palazzo, Concetto Spampinato
Date: 2023-12-06 00:00:00
Tasks: Continual Learning, Hippocampus

We propose Wake-Sleep Consolidated Learning (WSCL), a learning strategy leveraging Complementary Learning System theory and the wake-sleep phases of the human brain to improve the performance of deep neural networks for visual classification tasks in continual learning settings. Our method learns continually via the synchronization between distinct wake and sleep phases. During the wake phase, the model is exposed to sensory input and adapts its representations, ensuring stability through a dynamic parameter freezing mechanism and storing episodic memories in a short-term temporary memory (similarly to what happens in the hippocampus). During the sleep phase, the training process is split into NREM and REM stages. In the NREM stage, the model's synaptic weights are consolidated using replayed samples from the short-term and long-term memory and the synaptic plasticity mechanism is activated, strengthening important connections and weakening unimportant ones. In the REM stage, the model is exposed to previously-unseen realistic visual sensory experience, and the dreaming process is activated, which enables the model to explore the potential feature space, thus preparing synapses to future knowledge. We evaluate the effectiveness of our approach on three benchmark datasets: CIFAR-10, Tiny-ImageNet and FG-ImageNet. In all cases, our method outperforms the baselines and prior work, yielding a significant performance gain on continual visual classification tasks. Furthermore, we demonstrate the usefulness of all processing stages and the importance of dreaming to enable positive forward transfer.
Representation Learning in a Decomposed Encoder Design for Bio-inspired Hebbian Learning
Authors: Achref Jaziri, Sina Ditzel, Iuliia Pliushch, Visvanathan Ramesh
Date: 2023-11-22 00:00:00
Tasks: Inductive Bias, Representation Learning

Modern data-driven machine learning system designs exploit inductive biases in architectural structure, invariance and equivariance requirements, task-specific loss functions, and computational optimization tools. Previous works have illustrated that human-specified quasi-invariant filters can serve as a powerful inductive bias in the early layers of the encoder, enhancing robustness and transparency in learned classifiers. This paper explores this further within the context of representation learning with bio-inspired Hebbian learning rules. We propose a modular framework trained with a bio-inspired variant of contrastive predictive coding, comprising parallel encoders that leverage different invariant visual descriptors as inductive biases. We evaluate the representation learning capacity of our system in classification scenarios using diverse image datasets (GTSRB, STL10, CODEBRIM) and video datasets (UCF101). Our findings indicate that this form of inductive bias significantly improves the robustness of learned representations and narrows the performance gap between models using local Hebbian plasticity rules and those using backpropagation, while also achieving superior performance compared to non-decomposed encoders.
Learning with Chemical versus Electrical Synapses -- Does it Make a Difference?
Authors: Mónika Farsang, Mathias Lechner, David Lung, Ramin Hasani, Daniela Rus, Radu Grosu
Date: 2023-11-21 00:00:00
Tasks: Autonomous Driving

Bio-inspired neural networks have the potential to advance our understanding of neural computation and improve the state-of-the-art of AI systems. Bio-electrical synapses directly transmit neural signals, by enabling fast current flow between neurons. In contrast, bio-chemical synapses transmit neural signals indirectly, through neurotransmitters. Prior work showed that interpretable dynamics for complex robotic control, can be achieved by using chemical synapses, within a sparse, bio-inspired architecture, called Neural Circuit Policies (NCPs). However, a comparison of these two synaptic models, within the same architecture, remains an unexplored area. In this work we aim to determine the impact of using chemical synapses compared to electrical synapses, in both sparse and all-to-all connected networks. We conduct experiments with autonomous lane-keeping through a photorealistic autonomous driving simulator to evaluate their performance under diverse conditions and in the presence of noise. The experiments highlight the substantial influence of the architectural and synaptic-model choices, respectively. Our results show that employing chemical synapses yields noticeable improvements compared to electrical synapses, and that NCPs lead to better results in both synaptic models.
Nahid: AI-based Algorithm for operating fully-automatic surgery
Authors: Sina Saadati
Date: 2023-11-03 00:00:00

In this paper, for the first time, a method is presented that can provide a fully automated surgery based on software and computer vision techniques. Then, the advantages and challenges of computerization of medical surgery are examined. Finally, the surgery related to isolated ovarian endometriosis disease has been examined, and based on the presented method, a more detailed algorithm is presented that is capable of automatically diagnosing and treating this disease during surgery as proof of our proposed method where a U-net is trained to detect the endometriosis during surgery.
Randomized Kaczmarz with geometrically smoothed momentum
Authors: Seth J. Alderman, Roan W. Luikart, Nicholas F. Marshall
Date: 2024-01-17 00:00:00

This paper studies the effect of adding geometrically smoothed momentum to the randomized Kaczmarz algorithm, which is an instance of stochastic gradient descent on a linear least squares loss function. We prove a result about the expected error in the direction of singular vectors of the matrix defining the least squares loss. We present several numerical examples illustrating the utility of our result and pose several questions.
Mitigating distribution shift in machine learning-augmented hybrid simulation
Authors: Jiaxi Zhao, Qianxiao Li
Date: 2024-01-17 00:00:00

We study the problem of distribution shift generally arising in machine-learning augmented hybrid simulation, where parts of simulation algorithms are replaced by data-driven surrogates. We first establish a mathematical framework to understand the structure of machine-learning augmented hybrid simulation problems, and the cause and effect of the associated distribution shift. We show correlations between distribution shift and simulation error both numerically and theoretically. Then, we propose a simple methodology based on tangent-space regularized estimator to control the distribution shift, thereby improving the long-term accuracy of the simulation results. In the linear dynamics case, we provide a thorough theoretical analysis to quantify the effectiveness of the proposed method. Moreover, we conduct several numerical experiments, including simulating a partially known reaction-diffusion equation and solving Navier-Stokes equations using the projection method with a data-driven pressure solver. In all cases, we observe marked improvements in simulation accuracy under the proposed method, especially for systems with high degrees of distribution shift, such as those with relatively strong non-linear reaction mechanisms, or flows at large Reynolds numbers.
Monitoring Machine Learning Forecasts for Platform Data Streams
Authors: Jeroen Rombouts, Ines Wilms
Date: 2024-01-17 00:00:00
Tasks: Decision Making

Data stream forecasts are essential inputs for decision making at digital platforms. Machine learning algorithms are appealing candidates to produce such forecasts. Yet, digital platforms require a large-scale forecast framework that can flexibly respond to sudden performance drops. Re-training ML algorithms at the same speed as new data batches enter is usually computationally too costly. On the other hand, infrequent re-training requires specifying the re-training frequency and typically comes with a severe cost of forecast deterioration. To ensure accurate and stable forecasts, we propose a simple data-driven monitoring procedure to answer the question when the ML algorithm should be re-trained. Instead of investigating instability of the data streams, we test if the incoming streaming forecast loss batch differs from a well-defined reference batch. Using a novel dataset constituting 15-min frequency data streams from an on-demand logistics platform operating in London, we apply the monitoring procedure to popular ML algorithms including random forest, XGBoost and lasso. We show that monitor-based re-training produces accurate forecasts compared to viable benchmarks while preserving computational feasibility. Moreover, the choice of monitoring procedure is more important than the choice of ML algorithm, thereby permitting practitioners to combine the proposed monitoring procedure with one's favorite forecasting algorithm.
Fast parallel sampling under isoperimetry
Authors: Nima Anari, Sinho Chewi, Thuy-Duong Vuong
Date: 2024-01-17 00:00:00
Tasks: Point Processes

We show how to sample in parallel from a distribution $\pi$ over $\mathbb R^d$ that satisfies a log-Sobolev inequality and has a smooth log-density, by parallelizing the Langevin (resp. underdamped Langevin) algorithms. We show that our algorithm outputs samples from a distribution $\hat\pi$ that is close to $\pi$ in Kullback--Leibler (KL) divergence (resp. total variation (TV) distance), while using only $\log(d)^{O(1)}$ parallel rounds and $\widetilde{O}(d)$ (resp. $\widetilde O(\sqrt d)$) gradient evaluations in total. This constitutes the first parallel sampling algorithms with TV distance guarantees. For our main application, we show how to combine the TV distance guarantees of our algorithms with prior works and obtain RNC sampling-to-counting reductions for families of discrete distribution on the hypercube $\{\pm 1\}^n$ that are closed under exponential tilts and have bounded covariance. Consequently, we obtain an RNC sampler for directed Eulerian tours and asymmetric determinantal point processes, resolving open questions raised in prior works.
Trade-off Between Dependence and Complexity for Nonparametric Learning -- an Empirical Process Approach
Authors: Nabarun Deb, Debarghya Mukherjee
Date: 2024-01-17 00:00:00
Tasks: Weather Forecasting

Empirical process theory for i.i.d. observations has emerged as a ubiquitous tool for understanding the generalization properties of various statistical problems. However, in many applications where the data exhibit temporal dependencies (e.g., in finance, medical imaging, weather forecasting etc.), the corresponding empirical processes are much less understood. Motivated by this observation, we present a general bound on the expected supremum of empirical processes under standard $\beta/\rho$-mixing assumptions. Unlike most prior work, our results cover both the long and the short-range regimes of dependence. Our main result shows that a non-trivial trade-off between the complexity of the underlying function class and the dependence among the observations characterizes the learning rate in a large class of nonparametric problems. This trade-off reveals a new phenomenon, namely that even under long-range dependence, it is possible to attain the same rates as in the i.i.d. setting, provided the underlying function class is complex enough. We demonstrate the practical implications of our findings by analyzing various statistical estimators in both fixed and growing dimensions. Our main examples include a comprehensive case study of generalization error bounds in nonparametric regression over smoothness classes in fixed as well as growing dimension using neural nets, shape-restricted multivariate convex regression, estimating the optimal transport (Wasserstein) distance between two probability distributions, and classification under the Mammen-Tsybakov margin condition -- all under appropriate mixing assumptions. In the process, we also develop bounds on $L_r$ ($1\le r\le 2$)-localized empirical processes with dependent observations, which we then leverage to get faster rates for (a) tuning-free adaptation, and (b) set-structured learning problems.