What is pu.gg blogger

Here, cycle consistency refers to the construction of cycle graphs , in which the model constructs a series of edges from an initial node to other nodes and back again: Given a start node e. To do this, at the start of training, the model assumes that frames and text with the same timestamps are counterparts, but then relaxes this assumption later. The model then predicts a future state, and the node most similar to this prediction is selected.

Finally, the model attempts to invert the above steps by predicting the present state backward from the future node, and thus connecting the future node back with the start node. Intuitively, this training objective requires the predicted future to contain enough information about its past to be invertible, leading to predictions that correspond to meaningful changes to the same entities e. Moreover, the inclusion of cross-modal edges ensures future predictions are meaningful in either modality.

Importantly, this cyclic graph constraint makes few assumptions for the kind of temporal edges the model should learn, as long as they end up forming a consistent cycle. This enables the emergence of long-term temporal dynamics critical for future prediction without requiring manual labels of meaningful changes. Discovering Cycles in Real-World Video MMCC is trained without any explicit ground truth, using only long video sequences and randomly sampled starting conditions a frame or text excerpt and asking the model to find temporal cycles.

After training, MMCC can identify meaningful cycles that capture complex changes in video. We then rank all pairs according to this score and show the highest-scoring pairs of present and future frames detected in previously unseen videos examples below. We can use this same approach to temporally sort an unordered collection of video frames without any fine-tuning by finding an ordering that maximizes the overall confidence scores between all adjacent frames in the sorted sequence.

On CrossTask , a dataset of instruction videos with labels describing key steps, MMCC outperforms the previous self-supervised state-of-the-art models in inferring possible future actions. Conclusions We have introduced a self-supervised method to learn temporal dynamics by cycling through narrated instructional videos. An interesting future direction is transferring the model to agents so they can use it to conduct long-term planning.

When building a deep model for a new machine learning application, researchers often begin with existing network architectures, such as ResNets or EfficientNets. Instead, better performance potentially could be achieved by designing a new model that is optimized for the task. However, such efforts can be challenging and usually require considerable resources. We demonstrate that ensembles of even a small number of models that are easily constructed can match or exceed the accuracy of state-of-the-art models while being considerably more efficient.

What Are Model Ensembles and Cascades? Ensembles and cascades are related approaches that leverage the advantages of multiple models to achieve a better solution. Ensembles execute multiple models in parallel and then combine their outputs to make the final prediction.

Cascades are a subset of ensembles, but execute the collected models sequentially, and merge the solutions once the prediction has a high enough confidence. For simple inputs, cascades use less computation, but for more complex inputs, may end up calling on a greater number of models, resulting in higher computation costs. For example, the majority of images in ImageNet are easy for contemporary image recognition models to classify, but there are many images for which predictions vary between models and that will benefit most from an ensemble.

While ensembles are well-known , they are often not considered a core building block of deep model architectures and are rarely explored when researchers are developing more efficient models with a few notable exceptions [ 1 , 2 , 3 ]. Therefore, we conduct a comprehensive analysis of ensemble efficiency and show that a simple ensemble or cascade of off-the-shelf pre-trained models can enhance both the efficiency and accuracy of state-of-the-art models.

To encourage the adoption of model ensembles, we demonstrate the following beneficial properties:. So, we investigate whether an ensemble can be more accurate than a single model that has the same computational cost. The ensemble predictions are computed by averaging the predictions of each individual model.

This demonstrates that instead of using a large model, in this situation, one should use an ensemble of multiple considerably smaller models, which will reduce computation requirements while maintaining accuracy.

Moreover, we find that the training cost of an ensemble can be much lower e. In practice, model ensemble training can be parallelized using multiple accelerators leading to further reductions. This pattern holds for the ResNet and MobileNet families as well. Power and Simplicity of Cascades While we have demonstrated the utility of model ensembles, applying an ensemble is often wasteful for easy inputs where a subset of the ensemble will give the correct answer.

In these situations, cascades save computation by allowing for an early exit, potentially stopping and outputting an answer before all models are used.

The challenge is to determine when to exit from the cascade. To highlight the practical benefit of cascades, we intentionally choose a simple heuristic to measure the confidence of the prediction — we take the confidence of the model to be the maximum of the probabilities assigned to each class.

We use a threshold on the confidence score to determine when to exit from the cascade. To test this approach, we build model cascades for the EfficientNet , ResNet , and MobileNetV2 families to match either computation costs or accuracy limiting the cascade to a maximum of four models.

By design in cascades, some inputs incur more FLOPS than others, because more challenging inputs go through more models in the cascade than easier inputs. In some cases it is not the average computation cost but the worst-case cost that is the limiting factor.

By adding a simple constraint to the cascade building procedure, one can guarantee an upper bound to the computation cost of the cascade. See the paper for more details. Other than convolutional neural networks , we also consider a Transformer -based architecture, ViT. We build a cascade of ViT-Base and ViT-Large models to match the average computation or accuracy of a single state-of-the-art ViT-Large model, and show that the benefit of cascades also generalizes to Transformer-based architectures.

Earlier works on cascades have also shown efficiency improvements for state-of-the-art models, but here we demonstrate that a simple approach with a handful of models is sufficient. It is also important to verify that the FLOPS reduction obtained by cascades actually translates into speedup on hardware.

We examine this by comparing on-device latency and speed-up for similarly performing single models versus cascades. We find a reduction in the average online latency on TPUv3 of up to 5. As models become larger the more speed-up we find with comparable cascades.

While this highlights the simplicity of using ensembles, it also allows us to check all combinations of models in very little time so we can find optimal model collections with only a few CPU hours on a held out set of predictions.

When a large pool of models exists, we would expect cascades to be even more efficient and accurate, but brute force search is not feasible. However, efficient cascade search methods have been proposed. For example, the algorithm of Streeter , when applied to a large pool of models, produced cascades that matched the accuracy of state-of-the-art neural architecture search —based ImageNet models with significantly fewer FLOPS, for a range of model sizes.

In our paper we show more results for other models and tasks. For practitioners, this outlines a simple procedure to boost accuracy while retaining efficiency using off-the-shelf models. We encourage you to try it out! Kitani, Yair Alon prev. Movshovitz-Attias , and Elad Eban. Earlier this year, we launched Contactless Sleep Sensing in Nest Hub , an opt-in feature that can help users better understand their sleep patterns and nighttime wellness.

The human brain has special neurocircuitry to coordinate sleep cycles — transitions between deep, light, and rapid eye movement REM stages of sleep — vital not only for physical and emotional wellbeing, but also for optimal physical and cognitive performance.

Today we announced enhancements to Sleep Sensing that provide deeper sleep insights. Here we describe how we developed these novel technologies, through transfer learning techniques to estimate sleep stages and sensor fusion of radar and microphone signals to disambiguate the source of sleep disturbances.

Training and Evaluating the Sleep Staging Classification Model Most people cycle through sleep stages times a night, about every minutes, sometimes with a brief awakening between cycles. The key difference is that this new model was trained to predict sleep stages rather than simple sleep-wake status, and thus required new data and a more sophisticated training process.

In order to assemble a rich and diverse dataset suitable for training high-performing ML models, we leveraged existing non-radar datasets and applied transfer learning techniques to train the model. The gold standard for identifying sleep stages is polysomnography PSG , which employs an array of wearable sensors to monitor a number of body functions during sleep, such as brain activity, heartbeat, respiration, eye movement, and motion.

These signals can then be interpreted by trained sleep technologists to determine sleep stages. This similarity between the two domains makes it possible to leverage a plethysmography-based model and adapt it to work with radar. To do so, we first computed spectrograms from the RIP time series signals and used these as features to train a convolutional neural network CNN to predict the groundtruth sleep stages.

This model successfully learned to identify breathing and motion patterns in the RIP signal that could be used to distinguish between different sleep stages. This indicated to us that the same should also be possible when using radar-based signals. As expected, the model trained to predict sleep stages from a plethysmograph sensor was much less accurate when given radar sensor data instead.

However, the model still performed much better than chance, which demonstrated that it had learned features that were relevant across both domains. To improve on this, we collected a smaller secondary dataset of radar sensor data with corresponding PSG-based groundtruth labels, and then used a portion of this dataset to fine-tune the weights of the initial model. This smaller amount of additional training data allowed the model to adapt the original features it had learned from plethysmography-based sleep staging and successfully generalize them to our domain.

When evaluated on an unseen test set of new radar data, we found the fine-tuned model produced sleep staging results comparable to that of other consumer sleep trackers. More Intelligent Audio Sensing Through Audio Source Separation Soli-based sleep tracking gives users a convenient and reliable way to see how much sleep they are getting and when sleep disruptions occur. However, to understand and improve their sleep, users also need to understand why their sleep may be disrupted. To provide deeper insight into these disturbances, it is important to understand if the snores and coughs detected are your own.

Combining audio sensing with Soli-based motion and breathing cues, we updated our algorithms to separate sleep disturbances from the user-specified sleeping area versus other sources in the room.

Conversely, when snoring is detected outside the calibrated sleeping area, the two signals will vary independently. A user can then opt to save the outputs of the processing sound occurrences, such as the number of coughs and snore minutes in Google Fit, in order to view their night time wellness over time.

For example, a small feasibility study supported by the Cystic Fibrosis Foundation 2 is currently underway to evaluate the feasibility of measuring night time cough using Nest Hub in families of children with cystic fibrosis CF , a rare inherited disease, which can result in a chronic cough due to mucus in the lungs. Researchers are exploring if quantifying cough at night could be a proxy for monitoring response to treatment.

Conclusion Based on privacy-preserving radar and audio signals, these improved sleep staging and audio sensing features on Nest Hub provide deeper insights that we hope will help users translate their night time wellness into actionable improvements for their overall wellbeing.

Acknowledgements This work involved collaborative efforts from a multidisciplinary team of software engineers, researchers, clinicians, and cross-functional contributors.

Special thanks to Dr. Logan Schneider, a sleep neurologist whose clinical expertise and contributions were invaluable to continuously guide this research.

Jim Taylor, and the extended team. Thanks to Mark Malhotra and Shwetak Patel for their ongoing leadership, as well as the Nest, Fit, and Assistant teams we collaborated with to build and validate these enhancements to Sleep Sensing on Nest Hub.

As a Diamond Level sponsor of EMNLP , Google will contribute research on a diverse set of topics, including language interactions, causal inference, and question answering, additionally serving in various levels of organization in the conference.

We congratulate these authors, and all other researchers who are presenting their work at the conference Google affiliations presented in bold. That starts with the custom-made TPU integrated in Google Tensor that allows us to fulfill our vision of what should be possible on a Pixel phone.

We use neural architecture search NAS to automate the process of designing ML models, which incentivize the search algorithms to discover models that achieve higher quality while meeting latency and power requirements. This automation also allows us to scale the development of models for various on-device tasks.

Moreover, we have applied the same techniques to build a highly energy-efficient face detection model that is foundational to many Pixel 6 camera features. We customize the search space to include neural network building blocks that run efficiently on the Google Tensor TPU.

One widely-used building block in neural networks for various on-device vision tasks is the Inverted Bottleneck IBN. The IBN block has several variants, each with different tradeoffs, and is built using regular convolution and depthwise convolution layers. While IBNs with depthwise convolution have been conventionally used in mobile vision models due to their low computational complexity , fused-IBNs, wherein depthwise convolution is replaced by a regular convolution, have been shown to improve the accuracy and latency of image classification and object detection models on TPU.

However, fused-IBNs can have prohibitively high computational and memory requirements for neural network layer shapes that are typical in the later stages of vision models, limiting their use throughout the model and leaving the depthwise-IBN as the only alternative.

To overcome this limitation, we introduce IBNs that use group convolutions to enhance the flexibility in model design. While regular convolution mixes information across all the features in the input, group convolution slices the features into smaller groups and performs regular convolution on features within that group, reducing the overall computational cost. Faster, More Accurate Image Classification Which IBN variant to use at which stage of a deep neural network depends on the latency on the target hardware and the performance of the resulting neural network on the given task.

We construct a search space that includes all of these different IBN variants and use NAS to discover neural networks for the image classification task that optimize the classification accuracy at a desired latency on TPU.

Unlike accelerators such as the TPU, CPUs show a stronger correlation between the number of multiply-and-accumulate operations in the neural network and latency. Improving On-Device Semantic Segmentation Many vision models consist of two components, the base feature extractor for understanding general features of the image, and the head for understanding domain-specific features, such as semantic segmentation the task of assigning labels, such as sky, car, etc.

Image classification models are often used as feature extractors for these vision tasks. To further improve the segmentation model quality, we use the bidirectional feature pyramid network BiFPN as the segmentation head, which performs weighted fusion of different features extracted by the feature extractor.

The resulting models, named Autoseg-EdgeTPU, produce even higher-quality segmentation results, while also running faster. The final layers of the segmentation model contribute significantly to the overall latency, mainly due to the operations involved in generating a high resolution segmentation map. To optimize the latency on TPU, we introduce an approximate method for generating the high resolution segmentation map that reduces the memory requirement and provides a nearly 1. This search space also uses the non-trivial connection patterns seen in recent NAS works such as MnasFPN to merge different but related stages of the network to strengthen understanding.

Inclusive, Energy-Efficient Face Detection Face detection is a foundational technology in cameras that enables a suite of additional features, such as fixing the focus, exposure and white balance, and even removing blur from the face with the new Face Unblur feature. Such features must be designed responsibly , and Face Detection in the Pixel 6 were developed with our AI Principles top of mind. Since mobile cameras can be power-intensive, it was important for the face detection model to fit within a power budget.

Wechat is a privately owned company but China's tech giants must toe the Party line. Winnie the Pooh has actually fallen foul of the authorities here before. This renewed push against online Pooh is because we are now in the run-up to the Communist Party Congress this autumn. The meeting takes place every five years and, amongst other things, sees the appointment of the new Politburo Standing Committee: the now seven-member group at the top of the Chinese political system.

Xi Jinping will also be using the Congress, which marks the beginning of his second term in office, to further solidify his grip on power by promoting allies and sidelining those seen as a threat. It had been thought that China has transformed into a system of two-term governance for the country's supreme leader but this is merely a recent convention rather than a rule. So, because President Xi has made so many enemies within the Party as a result of his widespread anti-corruption crackdown, many have questioned whether he can afford to give up power after the next five-year term.

In order to stay on he will believe that he needs to ensure there are no cracks in the absolute loyalty he demands. And, in this climate, there is seen to be no room for even the most frivolous challenges to his supreme authority.

Court order bans 'tomato' insult. China tackles censor-busting services. Image source, Empics. You may also like:. The military parade posts China censored New Doctor Who prompts mixed reaction Court order bans 'tomato' insult.

Most Chinese citizens have simply never heard of him. Liu Xiaobo was better known abroad than he was in China. Related Topics. Xi Jinping China Censorship. Published 13 July Published 17 July