Generative normalizing flows are able to model multimodal spatial distributions, and they have been shown to model temporal correlations successfully as well. These models provide several benefits over other types of generative models due to their training stability, invertibility and efficiency in sampling and inference. This makes them a suitable candidate for stochastic spatio-temporal prediction problems, which are omnipresent in many fields of sciences, such as earth sciences, astrophysics or molecular sciences. In this paper, we present conditional normalizing flows for stochastic spatio-temporal modelling. The method is evaluated on the task of daily temperature and hourly geopotential map prediction from ERA5 datasets. Experiments show that our method is able to capture spatio-temporal correlations and extrapolates well beyond the time horizon used during training.
Foundation language models learn from their finetuning input context in different ways. In this paper, we reformulate inputs during finetuning for challenging translation tasks, leveraging model strengths from pretraining in novel ways to improve downstream performance. These reformulations are simple data level modifications, require no additional collection of training data or modification of data at inference time. They can be applied either on single language pair translation tasks or massively multilingual translation tasks. Experiments with these techniques demonstrate significant performance improvements up to $\textbf{3.5 chrF++ on the Flores200 translation benchmark}$. We hope our research accessibly improves finetuning data efficiency, enabling more effective training to scalably improve state-of-the-art performance. Our code is released $\href{https://github.com/bri25yu/LanguageModelExperimentation}{here}.$
The time and expense required to collect and label audio data has been a prohibitive factor in the availability of domain specific audio datasets. As the predictive specificity of a classifier depends on the specificity of the labels it is trained on, it follows that finely-labelled datasets are crucial for advances in machine learning. Aiming to stimulate progress in the field of machine listening, this paper introduces AeroSonicDB (YPAD-0523), a dataset of low-flying aircraft sounds for training acoustic detection and classification systems. This paper describes the method of exploiting ADS-B radio transmissions to passively collect and label audio samples. Provides a summary of the collated dataset. Presents baseline results from three binary classification models, then discusses the limitations of the current dataset and its future potential. The dataset contains 625 aircraft recordings ranging in event duration from 18 to 60 seconds, for a total of 8.87 hours of aircraft audio. These 625 samples feature 301 unique aircraft, each of which are supplied with 14 supplementary (non-acoustic) labels to describe the aircraft. The dataset also contains 3.52 hours of ambient background audio ("silence"), as a means to distinguish aircraft noise from other local environmental noises. Additionally, 6 hours of urban soundscape recordings (with aircraft annotations) are included as an ancillary method for evaluating model performance, and to provide a testing ground for real-time applications.
Precise 3D environmental mapping is pivotal in robotics. Existing methods often rely on predefined concepts during training or are time-intensive when generating semantic maps. This paper presents Open-Fusion, a groundbreaking approach for real-time open-vocabulary 3D mapping and queryable scene representation using RGB-D data. Open-Fusion harnesses the power of a pre-trained vision-language foundation model (VLFM) for open-set semantic comprehension and employs the Truncated Signed Distance Function (TSDF) for swift 3D scene reconstruction. By leveraging the VLFM, we extract region-based embeddings and their associated confidence maps. These are then integrated with 3D knowledge from TSDF using an enhanced Hungarian-based feature-matching mechanism. Notably, Open-Fusion delivers outstanding annotation-free 3D segmentation for open-vocabulary without necessitating additional 3D training. Benchmark tests on the ScanNet dataset against leading zero-shot methods highlight Open-Fusion's superiority. Furthermore, it seamlessly combines the strengths of region-based VLFM and TSDF, facilitating real-time 3D scene comprehension that includes object concepts and open-world semantics. We encourage the readers to view the demos on our project page: https://uark-aicv.github.io/OpenFusion
In this pioneering work we formulate ExpM+NF, a method for training machine learning (ML) on private data with pre-specified differentially privacy guarantee $\varepsilon>0, \delta=0$, by using the Exponential Mechanism (ExpM) and an auxiliary Normalizing Flow (NF). We articulate theoretical benefits of ExpM+NF over Differentially Private Stochastic Gradient Descent (DPSGD), the state-of-the-art (SOTA) and de facto method for differentially private ML, and we empirically test ExpM+NF against DPSGD using the SOTA implementation (Opacus with PRV accounting) in multiple classification tasks on the Adult Dataset (census data) and MIMIC-III Dataset (electronic healthcare records) using Logistic Regression and GRU-D, a deep learning recurrent neural network with ~20K-100K parameters. In all experiments, ExpM+NF achieves greater than 93% of the non-private training accuracy (AUC) for $\varepsilon \in [1\mathrm{e}{-3}, 1]$, exhibiting greater accuracy (higher AUC) and privacy (lower $\varepsilon$ with $\delta=0$) than DPSGD. Differentially private ML generally considers $\varepsilon \in [1,10]$ to maintain reasonable accuracy; hence, ExpM+NF's ability to provide strong accuracy for orders of magnitude better privacy (smaller $\varepsilon$) substantially pushes what is currently possible in differentially private ML. Training time results are presented showing ExpM+NF is comparable to (slightly faster) than DPSGD. Code for these experiments will be provided after review. Limitations and future directions are provided.
It is widely accepted that integrating intelligent reflecting surfaces (IRSs) with unmanned aerial vehicles (UAV) or drones can assist wireless networks in improving network coverage and end user Quality of Service (QoS). However, the critical constrain of drones is their very limited hovering/flying time. In this paper we propose the concept of robotic aerial IRSs (RA-IRSs), which are in essence drones that in addition to IRS embed an anchoring mechanism that allows them to grasp in an energy neutral manner at tall urban landforms such as lampposts. By doing so, RA-IRSs can completely eliminate the flying/hovering energy consumption and can offer service for multiple hours or even days (something not possible with UAV-mounted IRSs). Using that property we show how RA-IRS can increase network performance by changing their anchoring location to follow the spatio-temporal traffic demand. The proposed methodology, developed through Integer Linear Programming (ILP) formulations offers a significant Signal-to-Noise (SNR) gain in highly heterogeneous regions in terms of traffic demand compared to fixed IRS; hence, addressing urban coverage discrepancies effectively. Numerical simulations validate the superiority of RA-IRSs over fixed terrestrial IRSs in terms of traffic serviceability, sustaining more than 2 times the traffic demand in areas experiencing high heterogeneity, emphasizing their adaptability in improving coverage and QoS in complex urban terrains.
Object-based Novelty Detection (ND) aims to identify unknown objects that do not belong to classes seen during training by an object detection model. The task is particularly crucial in real-world applications, as it allows to avoid potentially harmful behaviours, e.g. as in the case of object detection models adopted in a self-driving car or in an autonomous robot. Traditional approaches to ND focus on one time offline post processing of the pretrained object detection output, leaving no possibility to improve the model robustness after training and discarding the abundant amount of out-of-distribution data encountered during deployment. In this work, we propose a novel framework for object-based ND, assuming that human feedback can be requested on the predicted output and later incorporated to refine the ND model without negatively affecting the main object detection performance. This refinement operation is repeated whenever new feedback is available. To tackle this new formulation of the problem for object detection, we propose a lightweight ND module attached on top of a pre-trained object detection model, which is incrementally updated through a feedback loop. We also propose a new benchmark to evaluate methods on this new setting and test extensively our ND approach against baselines, showing increased robustness and a successful incorporation of the received feedback.
A key benefit of deep vision-language models such as CLIP is that they enable zero-shot open vocabulary classification; the user has the ability to define novel class labels via natural language prompts at inference time. However, while CLIP-based zero-shot classifiers have demonstrated competitive performance across a range of domain shifts, they remain highly vulnerable to adversarial attacks. Therefore, ensuring the robustness of such models is crucial for their reliable deployment in the wild. In this work, we introduce Open Vocabulary Certification (OVC), a fast certification method designed for open-vocabulary models like CLIP via randomized smoothing techniques. Given a base "training" set of prompts and their corresponding certified CLIP classifiers, OVC relies on the observation that a classifier with a novel prompt can be viewed as a perturbed version of nearby classifiers in the base training set. Therefore, OVC can rapidly certify the novel classifier using a variation of incremental randomized smoothing. By using a caching trick, we achieve approximately two orders of magnitude acceleration in the certification process for novel prompts. To achieve further (heuristic) speedups, OVC approximates the embedding space at a given input using a multivariate normal distribution bypassing the need for sampling via forward passes through the vision backbone. We demonstrate the effectiveness of OVC on through experimental evaluation using multiple vision-language backbones on the CIFAR-10 and ImageNet test datasets.
Encoder-decoder transformer models have achieved great success on various vision-language (VL) tasks, but they suffer from high inference latency. Typically, the decoder takes up most of the latency because of the auto-regressive decoding. To accelerate the inference, we propose an approach of performing Dynamic Early Exit on Decoder (DEED). We build a multi-exit encoder-decoder transformer model which is trained with deep supervision so that each of its decoder layers is capable of generating plausible predictions. In addition, we leverage simple yet practical techniques, including shared generation head and adaptation modules, to keep accuracy when exiting at shallow decoder layers. Based on the multi-exit model, we perform step-level dynamic early exit during inference, where the model may decide to use fewer decoder layers based on its confidence of the current layer at each individual decoding step. Considering different number of decoder layers may be used at different decoding steps, we compute deeper-layer decoder features of previous decoding steps just-in-time, which ensures the features from different decoding steps are semantically aligned. We evaluate our approach with two state-of-the-art encoder-decoder transformer models on various VL tasks. We show our approach can reduce overall inference latency by 30%-60% with comparable or even higher accuracy compared to baselines.
Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurricanes and droughts. Parameters in the models that cannot be physically measured have a significant effect on the model output. For an idealized ocean model, we generated perturbed parameter ensemble data and trained surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity.