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1-800-Shared-Tasks 34

This paper presents a principled approach to finding flat minima. The motivation to seek such minima is due to their better generalization ability. The idea is to add to the original loss function a new term that exploits both width and depth of the objective function. In fact, the regularization term can be interpreted as Gaussian convolution of the exponentiated loss. Therefore, the introduced regularization term is essentially Gaussian smoothed version of the exponentiated loss. The smoothing obviously tends to suppress sharp minima. Overall, developing such regularization term based on thermodynamics concepts is very interesting. However, I would like to see more theoretical guarantees regarding convergence properties of the proposed method. Specifically, how does one prove that the iterates generated by the proposed method converge to a stationary point? Moreover, what are the conditions under which the proposed method converges faster than other methods (e.g., stochastic gradient descent)?

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The authors present a framework for online structure learning of sum-product network. Theyropose a novel algorithm that learns both the structure and parameters of the network incrementally, without requiring access to the entire dataset at once. The proposed method leverages the Gaussian nature of the leaves to efficiently learn the structure of the network. The authors demonstrate the effectiveness of their approach on several benchmark datasets and show that it outperforms existing methods in terms of computational efficiency and accuracy. However, I would like to see more discussion on the theoretical guarantees provided by the proposed method and how it scales to larger datasets.

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The invention of driverless cars is a really good idea. The driverless cars have many developments and improvements. Driverless cars can alert drivers to when something bad is about to happen. The cars are eco friendly because they use half the fuel that taxis use today. With all of the improvements it has, these cars will be one of the safest cars on the road. With all of the improvements on the driverless car the car will be safer. They have invented a track that sends signals to a reciever on the front end of the car. The reason for this is the senors to alert the driver. The senors tell the driver what is all around them. They have developed senors to help alert the driver whne something bad is about to happen. The senors have become more advanced, and are able to detect and respond to the danger of out of control skids or rollovers. The information from the senors can make the car apply the brakes on the individual wheels and reduce the engine's power. 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The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. 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The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless car. The sensors are the most important part of the driverless

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I think its a good idea to have driverless cars would be less car accidents the cars would change the world They would use less fuel and they have more flexibility than a bus The car announces the driver when he or she have to take over. The person in the car doesn't have to do all the work because the car helps them out the car is not all driverless because you can have a driver to take over in case of emergencies or complex driving situations. The potential benefits of driverless cars are numerous. They promise to increase safety by reducing human error, which is a leading cause of accidents. They could also improve fuel efficiency through optimized driving patterns and reduce congestion through coordinated traffic management. Furthermore, they offer increased flexibility, as they can be summoned on demand and used by those unable to drive, such as the elderly or disabled. However, there are also potential drawbacks. The cost of these vehicles and the infrastructure to support them could be prohibitive. There are also concerns about job losses in driving-related industries and the potential for these vehicles to be hacked or malfunction. The impact of driverless cars on society and the environment could be significant. They could reduce pollution and free up time currently spent driving for more productive activities. However, they could also lead to increased car use and urban sprawl, as driving becomes more convenient. In my perspective, while the potential benefits of driverless cars are exciting, it is crucial to address the potential drawbacks before they are adopted on a larger scale. We need to ensure that they are safe, affordable, and beneficial to society as a whole. We also need to consider the impact on employment and plan for a transition that is fair and just. In conclusion, driverless cars have the potential to revolutionize transportation, but careful planning and regulation will be needed to ensure that they are a benefit, not a detriment, to society.

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We thank all the reviewers for reading and commenting on the paper! AnonReviewer1: “It would be interesting to see some newer network architectures with skip connections such as ResNet, and DenseNet” Response: We released a converter to port Caffe model specs to Paleo, and thus Paleo now supports a wide range of CNNs and residual networks.his includes models like ResNet and DenseNet, which are known to benefit significantly from parallelization due to their hierarchical structure and large number of parameters. Please refer to our supplementary materials for more details.

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This submission introduces a formulation of Generative Adversarial Networks (GANs) under the lens of density ratio estimation, when using Bregman divergences..""" The authors introduce a new formulation of GANs, which is based on density ratio estimation. The authors show that the proposed formulation can be used to understand the behavior of GANs and to improve their performance. """

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The topic of the paper, model-based RL with a learned model, is important and timely. The paper is well written. I feel that the presented results are too incremental. Augmenting the frame prediction network with another head that predicts the reward is a very sensible thing to do..However, I would have liked to see a more extensive empirical evaluation of the proposed approach. The paper only shows results on five Atari games, and it would be beneficial to test the model on a larger set of games to evaluate its generalization capabilities. Additionally, it would have been helpful if the paper provided more details about the training process, such as hyperparameters and training times. Overall, the paper addresses an important problem in reinforcement learning but would benefit from further experimentation and additional technical details.

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Using an ensemble in the discriminator portion of a GAN is a sensible idea, and it is well explored and described in this paper. Further clarification andiscussion regarding how these ensembles operate would enhance the readability of the manuscript. For instance, what exactly does "forgiving teacher" mean? How do the different types of discriminator ensembles contribute to improved performance compared to single discriminator GANs? These points could benefit from further elucidation.

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How would you feel if you had a school that made you work nonstop for the whole day, it would be tiring. Thats why I think students shouldnt have to take classes without a class they are interested in. Schools should provide students with more opportunities to choose extracurricular classes and activities that align with their interests and talents, in order to improve their overall school experience and prevent burnout from nonstop academic work. Firstly, allowing students to choose extracurricular classes and activities that align with their interests and talents can help them develop a sense of purpose and belonging. When students are engaged in activities that they enjoy, they are more likely to feel motivated and invested in their education. This can lead to improved academic performance, as students are more likely to apply themselves to their studies when they are also passionate about their extracurricular pursuits. Secondly, providing students with more opportunities to choose extracurricular classes and activities can help to prevent burnout from nonstop academic work. Many students feel overwhelmed by the demands of academic life, and may struggle to find time for their own interests and passions outside of school. By offering a range of extracurricular options, schools can help students find a healthy balance between academic and personal pursuits, and reduce the risk of burnout. Finally, allowing students to choose extracurricular classes and activities that align with their interests and talents can help to foster creativity and innovation. When students are able to explore their passions and interests outside of the classroom, they are more likely to bring new ideas and perspectives to their academic work. This can lead to a more dynamic and engaging learning environment, where students are motivated to learn and grow. In conclusion, providing students with more opportunities to choose extracurricular classes and activities that align with their interests and talents is essential for improving their overall school experience and preventing burnout from nonstop academic work. By giving students the freedom to explore their passions and interests outside of the classroom, schools can help them develop a sense of purpose and belonging, prevent burnout, and foster creativity and innovation.

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The proposed approach consists in a greedy layer wise initialization strategy for a deep MLP model, which is followed by global gradient-descent with dropout for fine-tuning. The initialization strategy uses a first randomly initialized sigmoid layer for dimensionality expansion followed by 2 sigmoid layers whose weights are initialized by Marginal Fisher Analysis (MFA) which learns a linear dimensionality reduction based on a neighborhood graph constructed using class label information (i.e. supervised dimensionality reduction). Output layer is a standard softmax layer. The approach is thus to be added to a growing list of heuristic layer-wise initialization schemes. The particular choice of initialization strategy, while reasonable, is not sufficiently well motivated in the paper relative to alternatives, and thus feels rather arbitrary. The paper lacks clarity in the paper lacks clarity in the description of the MDA framework. While it is mentioned that MDA is based on stacked feature learning models and utilizes marginal Fisher analysis (MFA) for layer-wise initialization, the details of how MFA is implemented and incorporated into the deep architecture are not provided. It would be beneficial to explain the specific steps involved in learning the weight matrices of MFA layer by layer and how the initialization process contributes to the overall performance of MDA. Additionally, the authors mention the use of deep learning techniques such as backpropagation, dropout, and denoising for fine-tuning the network, but there is no elaboration on how these techniques are applied or their impact on the results. Providing more information about the implementation details of MDA and the specific utilization of these techniques would improve the understanding of the proposed framework. Moreover, while the abstract states that MDA outperforms both shallow feature learning models and state-of-the-art deep learning models in various applications, the experimental results and comparative analysis are not presented in the partial review. It is essential to include a summary of the experiments conducted and highlight the specific findings that demonstrate the superior performance of MDA. Additionally, mentioning the datasets used for evaluation and the evaluation metrics employed would provide a clearer understanding of the experimental setup. The inclusion of these results and analysis would strengthen the claims made in the abstract and provide empirical evidence for the effectiveness of MDA.In terms of organization and structure, the partial review mentions the initialization strategy and briefly comments on its motivation, but it lacks a coherent flow and transitions between ideas. It would be beneficial to reorganize the review by discussing the initialization strategy and its motivation in a more systematic and logical manner. Additionally, providing a clear overview of the structure of the paper and its sections would improve the readability and coherence of the review.Overall, the partial review provides a basic understanding of the proposed MDA framework, but it lacks clarity and detail in key aspects such as the implementation of MFA and the application of deep learning techniques for fine-tuning. Additionally, the inclusion of experimental results and analysis would strengthen the claims made in the abstract. With some revisions and further elaboration, the review could provide a more comprehensive assessment of the paper.

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The authors present a framework for online structure learning of sum-productetworks (SPNs) with Gaussian leaves. They propose an algorithm that learns the parameters of the SPN incrementally, without requiring access to the entire dataset at once. The proposed method leverages recent advances in offline structure learning methods for SPNs and adapts them to the online setting. The authors demonstrate the effectiveness of their approach on several benchmark datasets and show that it outperforms existing state-of-the-art methods in terms of both accuracy and computational efficiency. Overall, this work makes an important contribution to the field of machine learning and has significant implications for real-world applications where data is constantly changing or streaming in.

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My position on driverless cars is not approved. If the car is called "driverless" why would you still need a human in the car to be ready and alerted if an he/she had to take action in a serious situation. "Designed to notify the driver wjhen the road ahead requires human skills, such as navigating through work zones and around accidents." Since the car is a driverless car why would a driver still need to be ready and alerted when the car cant manage to handle something. As in paragraph 7 it states "all are designed to notify the driver when the road ahead requires human skills, such as navigating through work zones and around accidents." Well if thats the case then why call it a driverless car if you still need to be alerted. That would also mean that the driver is not allowed to slack off and that the rules before the human got that car will still applie. Both eyes on the road, hands on the wheel, and no distractions. If the car is driverless, then the driver should be able to do whatever they want without having to worry about the road ahead. The cost and practicality of upgrading existing roads or building new ones to accommodate these cars is another factor that makes me skeptical about the feasibility of driverless cars. The infrastructure required to support these vehicles is extensive and expensive. It's not just about the cars themselves, but the entire ecosystem that needs to be built around them. This includes road sensors, communication systems, and traffic management systems, all of which come with a hefty price tag. In a world where many cities are struggling to maintain their existing infrastructure, the idea of investing billions into new technology seems impractical. Moreover, the potential risks and limitations of relying on sensors and technology to navigate roads are significant. While technology has made great strides in recent years, it is not infallible. Sensors can fail, software can have bugs, and systems can be hacked. In adverse weather conditions or power outages, the reliability of these systems is questionable. A heavy snowfall or a sudden downpour can easily obscure a sensor's view, making it difficult for the car to navigate. In the event of a power outage, the car could potentially be left stranded, leaving the passenger in a vulnerable position. Furthermore, the ethical implications of driverless cars cannot be ignored. In a situation where an accident is inevitable, who does the car choose to save? The passenger, the pedestrian, or the occupants of another vehicle? These are questions that technology cannot answer. In conclusion, while driverless cars may seem like an attractive prospect, they are not truly "driverless". They still require human intervention in certain situations, and the cost and practicality of implementing the necessary infrastructure is daunting. The potential risks and limitations of relying on technology to navigate roads, particularly in adverse weather conditions or power outages, are significant. Until these issues are addressed, I remain skeptical about the feasibility and safety of driverless cars.

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There is a bit of a spread in the reviewer scores and unfortunately it wasn't possible to entice the reviewers to participate in a discussion..""" The authors propose a novel technique, called Neuro-Symbolic Program Synthesis, to overcome these problems. Once trained, their approach can automatically construct computer programs in a domain-specific lanaguage that are consistent with a set of input-output examples. Their method is based on two neural modules. The first module, the cross correlation I/0 network, given a set of input/output examples, produces a continous representation of the set of I/O exmaples. The second module, called the Recursive- Reverse-Recursive Network (R3NN) given the continous representation of the examples, synthesises a program by incrementally expaning partial programs. They demonstrate the effectiveness of their approach by applying it to the domain of regular expression based string transformations. Experiments show the R3NN model can construct programs from new input/output examples and construct new programs for tasks it has never observed during training. The paper is well written and the authors provide a clear and convincing argument for the need for their proposed approach. However, there are some issues with the paper that should be addressed before publication. First, the authors do not provide any evidence that their approach is more effective than other approaches. For example, they do not compare their approach to other approaches such as symbolic program induction or machine learning. Second, the authors do not provide any evidence that their approach is more efficient than other approaches. For example, the authors do not compare their approach to other approaches in terms of runtime or memory usage. Third, the authors do not discuss the potential drawbacks of their approach. For example, they don't discuss the possibility that their approach may produce incorrect programs. Fourth, the authors do not

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Driverless cars would be an amazing new technology to introduce to the world. The ability to simply call a taxi and have a driverless car pick you up and take you wherever you wish, whenever you wish will revolutionize the way people get around. The technology may be in its primitive stages as of now, but soon driverless cars can help to automate many tasks that are currently being done by humans. Driverless cars will be an excellent addition to the modern world. Not only does the driverless car make sense practically, it also has many safety benefits. According to the National Highway Traffic Safety Administration, there were 32,367 traffic fatalities in the United States in 2011, and 33,561 in 2012. In 2013, this number decreased to 32,719, but still, far too many lives were lost. Most of these accidents are caused by human error, whether it is drunk driving, speeding, or simply not paying attention. With a driverless car, all of these errors can be eliminated. A driverless car will never get a DUI, never speed, and never fall asleep at the wheel. It will always be alert and ready to react to any situation that arises on the road. Not only will driverless cars decrease the amount of fatalities on the road, but they will also decrease the amount of minor accidents as well. No more fender benders from someone not paying attention while parking, no more side swiping because someone changed lanes without checking their blind spot, and no more rear ending because someone did not leave enough space between them and the car in front of them. The roads will be a much safer place once driverless cars become the norm. However, there are some downsides to driverless cars as well. One of the main concerns with driverless cars is job loss. Many people make a living off of driving, whether it is truck drivers, taxi cab drivers, or even Uber drivers. With the introduction of driverless cars, all of these jobs will become obsolete. This means that thousands, maybe even millions of people will lose their jobs. This could lead to an increase in poverty and unemployment, which could have a negative effect on society. Another concern with driverless cars is societal acceptance. While the idea of a driverless car may seem exciting to some, others may be hesitant to give up control of their vehicle. Many people enjoy the feeling of being in control of their car, and the thought of a machine taking over that control may be unsettling. Additionally, there may be concerns about the reliability of the technology. What if the car's sensors malfunction? What if the car makes a mistake and gets into an accident? These are all valid concerns that must be addressed before driverless cars become mainstream. Major car manufacturers such as Tesla, Ford, and Toyota have already begun investing billions of dollars into the research and development of driverless cars. Tesla has already released a semi-autonomous car that can drive itself on the highway, and Ford has announced plans to release a fully autonomous car by 2020. It is clear that driverless cars are the future of transportation, and while there are some concerns that need to be addressed, the benefits of driverless cars far outweigh the drawbacks. They will make the roads a safer place, decrease traffic congestion, and provide a mode of transportation for those who are unable to drive themselves. While job loss and societal acceptance are concerns, they are not reasons enough to halt the progress of driverless cars. Instead, we should embrace this new technology and work towards making it a safe and reliable mode of transportation for everyone.

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I think the problem here is well motivated, the approach is insightful and intuitive, and the results are convincing of the approach (although lacking in variety of applications). I like the fact that the authors use POS and NER in """ their experiments, but I would have liked to see more details about how they chose the features. I also think that the authors should provide more details about the training process, especially the hyperparameters used. """ Conclusion: """ Overall, this is a very interesting paper that makes an important contribution to the field. I would recommend it for publication in a top-tier conference. """

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Imagine a world where people no longer control their vehicles, the machines they used to buy for the pleasure of driving and not just to get around while playing on their phones. Terrifying right? With the idea of driverless cars coming into view in this modern day and age we must pause from imagining the cool idea of these automated automobiles, and imagine the scary possibilities that very likely could come along too. Many of the people who push for driverless cars only see the idea of a car coming to them withouth them even having to move a muscle. However, these people most likely haven't conscidered the dangers and downfalls of the self driving cars. we as a people will become lazy, forget the enjoyment that can come from driving most importantly create many new risks and dangers in the driving world. These days many people are already too lazy to even walk, but imagine if people no longer even have to drive. With automated cars becoming a possibility many are losing their minds over the idea of being able to get around without having to put forth any effort. These people may have learned to drive at one point, but with automated cars they may forget how to drive causing huge problems if they ever need to drive a car that can't drive itself. If we create these self driving cars we will end up with an entirely new breed of lazy people . Not only will people become lazy, but they will also lose the enjoyment that comes with driving. Driving can be a fun activity for many people, but with self driving cars there will no longer be a reason to enjoy the thrill of the ride. People won't be able to feel the wind in their hair, the sun on their face, or the rush of adrenaline as they speed down the highway. Instead, they will be confined to the back seat of a car, unable to experience the joy of driving. Furthermore, self driving cars pose a significant risk of malfunction and accidents. While proponents of self driving cars argue that they will be safer than human driven vehicles, this is not necessarily true. Computers can malfunction, sensors can fail, and accidents can still happen. In fact, there have already been several accidents involving self driving cars, some of which have resulted in fatalities. Additionally, if a self driving car were to malfunction, it could potentially cause more damage than a human driven vehicle, as it would be unable to react to the situation in the same way a human driver could. For example, if a self driving car were to suddenly accelerate out of control, it could potentially cause a devastating accident, whereas a human driver would be able to react and take control of the vehicle to prevent such an accident. In conclusion, while the idea of self driving cars may seem appealing to some, it is important to consider the potential risks and downfalls associated with them. Creating a new breed of lazy individuals, taking away the enjoyment of driving, and posing a significant risk of malfunction and accidents are all valid reasons to reconsider the implementation of self driving cars. While the benefits of self driving cars, such as increased safety and convenience, are certainly appealing, they do not outweigh the potential risks. It is important to proceed with caution and carefully consider the potential consequences before implementing self driving cars on a large scale.

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The reviewers generally agreed that the research direction pursued in the paper is a valuable one, but all reviewers expressed strong reservations about the value of a linear probe on intermediate features. The lack of flexibility in the probing method limits its ability to provide meaningful insights into the roles and dynamics of the intermediate layers. Several reviewers suggested that a non-linear probe would be more appropriate for capturing the complex relationships within these layers. Additionally, there were concerns about the inability of the probes to affect the training phase, as this may hinder their ability to detect subtle changes or improve the model during training. Overall, while the concept of using probes to understand intermediate layers is promising, the use of linear classifiers raises doubts about their effectiveness and potential limitations.

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As discussed, the there are multiple concurrent contributions in different packages/submission by the authors that are in parts difficult to disentangle. Despite this fact, it is impressive to see a system learning from natural feedback in an online fashion. To the best of my knowledge, this is a new quality of result that was achieved - in particular as close to full supervision results are reached in some cases in this less constraint setting. several points were raised that were in turn addressed by the authors: 1. formalisation of the task (learning dialogue) is not precise. when can we declare success? The answer of the authors is partially satisfying. For this particular work, it might make sense to more precisely set goals e.g. to be as good as full supervision. 2. (along the line of the previous question:) dialogue can be seen as a form of noisy supervision. can you please report the classic supervision baselines for the particular model used? this would give a sense what fraction of the dialogue can be seen as a form of noisy supervision. Can you please report the classic supervision baselines for the particular model used? This would give us a sense of what fraction of the Bot's performance is due to its interaction with humans compared to simply training on labeled data. Additionally, it would be interesting to explore how the Bot's performance scales with more dialogue interactions. Does the improvement plateau after a certain number of interactions, or does it continue to improve indefinitely? Answering these questions would provide valuable insights into the efficacy of dialogue learning with a human-in-the-loop. Another point worth discussing is the design of the simulator used in the experiments. While the authors mention that a synthetic environment was created to test various aspects of learning, no details are provided about the specific scenarios or tasks that were simulated. It would be beneficial to have a clear description of the simulator, including the types of dialogues it was able to generate and the range of teacher feedback it provided. This information would help assess the generalizability of the proposed approach and its applicability to real-world dialogue scenarios. Lastly, the validation of the approach through real experiments with Mechanical Turk is commendable. However, more information is needed regarding the experimental setup and the specific criteria used to evaluate the Bot's performance. How was the feedback from Mechanical Turk workers collected and analyzed? What metrics were used to measure the Bot's question-answering ability? Providing these details would enhance the transparency and reproducibility of the study's findings. Overall, this paper presents an important and novel contribution to the field of dialogue learning. The results demonstrate the potential of interactive learning with a human-in-the-loop, but further investigation is needed to fully understand the impact of this approach. Addressing the mentioned points and providing additional insights into the experimental setup and evaluation metrics would strengthen the paper and make it more valuable to the research community.

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This paper extend the Spin Glass analysis of Choromanska et al. (2015a) to Res Nets which yield the novel dynamic ensemble results for Res Nets and the connection to Batch Normalization and the analysis of their loss surface of Res Nets. The paper is well-written with many insightful explanation of results. However, there are some minor issues that could be improved upon: * In section 3, the authors claim that "the main mechanism that controls the dynamic ensemble behavior is the scaling introduced, e.g., by the Batch Normalization technique". While this statement is true, it would have been helpful if the authors provided more details on how exactly Batch Normalization introduces scaling and why this leads to the observed behavior. * In section 4, the authors mention that they employed generalized spin glass models to study the number of critical points in the optimization of Residual Networks. However, they do not provide any detail on what these models are or how they were used. A brief explanation of these models and their relevance to the topic would help readers better understand the significance of the findings.

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The paper presents a novel strategy to deal with dynamic computation graphs. They arise, when the computation is dynamically influenced by the input data, such as in LSTMs. The authors propose an `unrolling' strategy over the operations done at every step, which allows a new kind of batching of inputs. The presented idea is novel and the results clearly indicate the potential of the approach..""" However, the paper does not provide any details about the implementation of the unrolling strategy. It is unclear how the unrolling is done and what the computational cost is. Furthermore, the paper does not discuss the impact of the unrolling on the performance of the model. It would be interesting to see if the unrolling strategy can improve the performance of the model compared to a static computation graph. """

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I think the community service is a wonderful idea The children should know that littering is a bad habit and that it is our responsibility to keep our surroundings clean. This can be taught effectively by involving them in community service. For instance, students can participate in a local park clean-up drive. This not only helps in maintaining the cleanliness of the park but also instills a sense of responsibility in the students towards their environment. Moreover, community service can also foster a sense of unity and cooperation among students. For example, organizing a charity event for a local orphanage can teach students the importance of teamwork and empathy. They learn to work together for a common cause and understand the value of giving back to the community. Furthermore, community service can significantly contribute to personal growth and development. It can help students develop essential life skills such as communication, problem-solving, and time management. For instance, a student leading a recycling campaign in their school will learn to communicate effectively, solve problems that arise during the campaign, and manage their time efficiently. In conclusion, requiring students to participate in community service can have numerous benefits. It can help in environmental conservation, foster a sense of community, and contribute to personal growth and development. Therefore, it is essential to incorporate community service into the school curriculum.

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The authors pointed out some limitations of existing deep architectures, in particular hard to optimize on small or mid size datasets, and proposed to stack marginal fisher analysis (MFA) to build deep models. The proposed method is tested on several small to mid size datasets and compared with several feature learning methods. The authors also applied some existing techniques in deep learning, such as backprop, denoising and dropout to improve performance. The new contribution of the paper is limited. MFA has long been proposed. The authors fail to theoretically or empirically justify the stacking of MFAs. Moreover, they did not compare their model with other state-of-the-art feature learning methods which may be more appropriate for small/mid size datasets. Finally, there is no discussion about the scalability of the proposed approach.

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The paper presents a second-order method for training a neural networks while ensuring at the same time that weights (and activations) are binary. Through binarization, the method aims to achieve model compression for subsequent deployment on low-memory systems. The method is abbreviated BPN for "binarization using proximal Newton algorithm". The method incorporates the supervised loss function directly in the binarization procedure, which is an important and desirable property. (Authors mention that existing weight binarization methods ignore the effect of binarization to the loss.) The method is clearly described and related analytically to the previously proposed weight binarization methods. The experiments are extensive with multiple datasets and architectures, and demonstrate the generally higher performance of the proposed approach. A minor issue with the feed-forward network experiments is that only test errors are reported. Reporting train error as well would provide additional insight into convergence behavior of the different approaches. One concern I have is about the computational cost of the proposed method compared to other state-of-the-art binarization techniques. While the authors claim that their method is computationally efficient due to its reliance on closed-form solutions and reuse of computations done by popular optimizers like Adam, they do not quantify nor compare the actual running times required by each method. A thorough comparison including wall-clock time measurements should help solidifying their claims.

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I think you should let us students that have a D should play sports. I think you not let kids with a F should not play because then need to have higher than a F in the grade book I think we should have more activities so everyone would come to school everyday i don't know u might have a lot of new kids that don't want to come to school but if you have more activities they might want to come to school. Tutoring is good for kids that need help with their grades because it helps them learn what they need to do to get better grades. Field trips are fun for kids because they get to go out of the classroom and do something different. Please complete the essay by addressing the following points: * Discuss the potential benefits and drawbacks of allowing students with a D grade to participate in sports. * Discuss the potential benefits and drawbacks of offering more activities to encourage attendance. * Provide evidence to support your position on both issues. * Address the role of tutoring and field trips in promoting student success and engagement. * Conclude with a clear opinion on both issues and a summary of the main points discussed in the essay. Note: Please use proper grammar, spelling, and punctuation throughout the essay.

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In the ruels of voteing there should be change. When voting citesens's votes should matter, but they dont Whats the point of even voting when your vote doesnt help who you want to be president win. Why should people in office get to have the say? Its not fair for those pople to chose everyones prersednt . Everyone in the United Dtates should have the say of who runs them for four years. No one wants someone who they don't even like calling the shots. Also when we don't get to choose the the people that do choose make it go in there favor and not ours. This country is all of ours and sould not have all the disions made by only a few people. Everyones vote should matter. Note: The above partial essay is just a starting point. You will need to expand on the ideas presented and provide evidence to support your argument for or against the need for change in the rules of voting.

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Dear authors, congratulations on this very interesting paper; using auxiliary tasks is a great idea that opens a lot of future research. However I'm having troubles understanding the exact contribution of your work. Could you please clarify what are the main differences between your approach and standard deep Q-network (DQN) or policy gradient methods? Also, how does your method compare to recent advances in multi-task/meta-learning? I think some more explanations would be helpful to fully appreciate the novelty and potential impact of your proposed method. Best regards, [Your Name]

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All three reviewers appeared to have substantial difficulties understanding the proposed approach due to unclear presentation. One reviewer mentioned that "the writing style makes me feel like I'm trying to read a poem written in a language I don't understand." Another reviewer suggested that the authors should provide more explanations or examples to help readers follow their ideas. The third reviewer criticized the lack of sufficient experimental results to support the claims made in the paper. Specifically, this reviewer pointed out that there was no comparison between the proposed method and other state-of-the-art methods in terms of computational cost and accuracy. Therefore, all three reviewers recommended major revisions before publication can be considered.

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The paper proposes using an actor-critic RL algorithm for training learning rate controllers for supervised learning. The proposed method outperforms standard optimizers like SGD, ADAM and RMSprop in experiments conducted on MNIST and CIFAR 10. I have two main concerns. One is the lack of comparisons to other state-of-the-art methods for learning rate control. While the authors claim their approach outperforms existing methods, they do not provide any concrete evidence or comparison results to support this claim. Two, I am concerned with how well the actor-critic architecture scales to more complex tasks beyond image classification. Will the same approach work equally well when applied to tasks like natural language processing or speech recognition?

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Variational auto-encoders, adversarial networks, and kernel scoring rules like MMD have recently gained popularity as methods for learning directed generative models and for other applications like domain adaptation. This paper gives an additional method along the scoring rules direction that uses the matching of central moments to match two probability distributions. The technique is simple, and in the case of domain adaptation, highly effective. CMD seems like a very nice and straightforward solution to the domain adaptation problem. The method is computationally straightforward to implement, and seems quite stable with respect to the tuning parameters when compared to MMD. I was skeptical reading through this, especially given the fact that you only use K=5 in your experiments, but the results seem quite good. The natural question that I have now is: how will this method do in training generative models?Will we see similar gains there as well?

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This work address the problem of supervised learning from strongly labeled data with label noise. This is a very practical and relevant problem in applied machine learning. The authors note that using sampling approaches such as EM isn't effective, too slow and cannot be integrated into end-to-end training. Thus, they propose to simulate the effects of EM by a noisy adaptation layer, effectively a softmax, that is added to the architecture during training, and is omitted at inference.""" The authors propose to use a softmax layer to simulate the effects of the EM algorithm. They show that this approach outperforms previous methods, and that it is faster than the EM algorithm. """ Conclusion: """ In conclusion, the authors propose a novel approach to training neural networks from noisy data. They show that their approach outperforms previous approaches, and that it is much faster than the EM algorithm, which is the state-of-the-art method for this problem. """

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This was a borderline paper. However, no reviewers were willing to champion the acceptance Despite being a borderline paper, there was no sufficient support from the reviewers to advocate for its acceptance. However, the paper addresses an important issue of adjusting for dropout variance in weight initialization and Batch Normalization, which can potentially lead to higher accuracy. The authors propose a novel approach by correcting for the influence of dropout rates and nonlinearity on variance. They also update the variance moving averages of Batch Normalization after training with dropout off, resulting in state-of-the-art performance on CIFAR-10 and CIFAR-100 without data augmentation. Overall, further evaluations and refinements are needed to strengthen the paper's case for acceptance.

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In the last few years vehicle emissions have been heating and even destroying our atmosphere. Cities and even whole countries are taking precautions to lower the amount of fuel emissons recorded in the air. It's not only that people are trying to cut back to help the enviroment they just arent driving as much anymore for some reason, even in areas that driving would be more useful than other forms of transportation. You may want to cut back after seeing why they did too. In Germany there is a suburban city that has given up their use of vehicles. One of the local residents stated "When I had a car I was always tense, I'm much happier this way." You may not notice it but the hustle and bustle of everyday life and commuting may just be putting more stress on you. Cutting out personal commute will not only be making your life less stressful it will also help clear the air in some cities as well. Passenger cars are responsible for about 60% of transportation-related greenhouse gas emissions. Therefore, reducing personal commute can significantly decrease the amount of harmful gases released into the atmosphere. Alternative transportation methods such as cycling, walking, or using public transport can significantly reduce the carbon footprint. For instance, Copenhagen, a city in Denmark, has more than half of its population commuting by bicycle. This has not only reduced the city's carbon emissions but also improved the overall health of its residents due to increased physical activity. Similarly, Singapore has implemented an efficient public transportation system and promoted the use of electric vehicles to reduce emissions. The city-state has seen a significant improvement in air quality and a reduction in noise pollution. Moreover, the use of electric vehicles (EVs) is another effective measure to reduce vehicle emissions. Countries like Norway have heavily invested in EV infrastructure, leading to more than half of all new cars sold in 2020 being electric. This has significantly reduced the country's carbon emissions and set a precedent for other countries to follow. In conclusion, the impact of vehicle emissions on the environment is profound and detrimental. However, with the implementation of alternative transportation measures and the reduction of personal commute, we can significantly reduce these emissions. Not only does this benefit the environment by reducing pollution and slowing climate change, but it also improves individual well-being by reducing stress and promoting physical activity. The success stories of cities and countries that have implemented these measures serve as a beacon of hope and a model for others to emulate.

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How does the Electoral College affect the selection of the presidency and is it a positive force? Many may answer "No." or "I don't know." to these questions, only doing such because that's what they've been or what they've heard others say. While many have negative feelings, or no feelings at all, toward the Electoral College, I believe it plays a crucial role in our presidential elections and is, in fact, a positive force. One benefit of the Electoral College is its transregional approval system. Without the Electoral College, presidential candidates could simply campaign in heavily populated areas and ignore the rest of the country. With the Electoral College, however, candidates must consider the needs and opinions of voters across the country, not just those in heavily populated areas. This ensures that the president is elected by a broad coalition of citizens, rather than just a single region or group of people. Another benefit of the Electoral College is the role of swing states. Swing states are states that are not consistently "red" (Republican) or "blue" (Democratic) and are therefore critical in determining the outcome of a presidential election. The importance of swing states forces candidates to pay attention to the needs and concerns of voters in those states, which helps to ensure that the president is elected by a broad coalition of citizens. A third benefit of the Electoral College is the certainty it provides over popular voting. Without the Electoral College, it is possible that a presidential election could be decided by a single vote, leading to chaos and uncertainty. With the Electoral College, however, the outcome of the election is clear and certain, as the candidate who wins the most electoral votes wins the presidency. Despite these benefits, some argue that the Electoral College is undemocratic and that a popular vote system would be more fair. However, a popular vote system would lead to candidates campaigning only in heavily populated areas, ignoring the needs and concerns of voters in smaller states and rural areas. The Electoral College ensures that all voters, regardless of where they live, have a voice in the presidential election. In conclusion, the Electoral College plays a crucial role in the selection of the President of the United States and is a positive force in our democracy. Its transregional approval system, swing states, and certainty over popular voting ensure that the president is elected by a broad coalition of citizens, rather than just a single region or group of people. While some may argue that a popular vote system would be more fair, the Electoral College ensures that all voters have a voice in the presidential election and helps to ensure that the president is elected by a broad coalition of citizens. Sources: * "The Electoral College: A 2020 Perspective." The Atlantic, 26 Nov. 2020, <https://www.theatlantic.com/politics/archive/2020/11/electoral-college-2020-perspective/617154/> * "The Pros and Cons of the Electoral College." ThoughtCo, <https://www.thoughtco.com/pros-and-cons-of-the-electoral-college-3974429> * "The Electoral College: Why It's Still Important." The New York Times, 18 Nov. 2020, <https://www.nytimes.com/2020/11/18/opinion/electoral-college-2020.html>

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People all around the world go through different things, but it's those who are there with you that help guide the way In times of a tough decision, seeking multiple opinions will help in making a better choice Everyones opinion gives a different point of view and can help pick out what decision is best As well as, some people have gone through similar things and can help talk out the situation In my lifetime I have experienced my friend go through a rough path. Because of the people who helped her, she is now happier and living much better. Many people will have different perspectives. They all can influence a decision, because everyone is different. We are raised by our culture and environment. Therefore, no two minds are alike. Situations are seen differently, giving an abundance of different solutions. Imagine a parent and friend helping out the same person, now compare the two. As a parent they want what's best for their child and try to look to steer them in the right direction. While a friend may want the same thing, but at the end of the day they are looking to have fun and may not always give the best advice. When people are getting someone else's a opinion they are making themselves vulnerable to the fact that different possibilities could happen. People can get strongly judged or rejected on their opinions they've made, as that could heavily impact a choice. However, getting multiple views on a decision is always the best option, because everyone sees things differently. Throughout our life people have been through hardships. Some may be similar situations which include ways it's been dealt with in hopes of others making better choices. With the help of someones past, it can help pave a way for someones decision. Knowing peoples mistakes and consequences can differ what others choose to do. When making a choice some people visit professionals. A few include psychologists, therapists and counselors, they help listen to a problem in hopes of breaking it down and seeking best possible outcomes. These people are there to help drive a path for healthy decision making. Another reason why seeking multiple opinions is important is because it helps to broaden our understanding and perspective. It allows us to see things from different angles and consider aspects that we might not have thought of on our own. For instance, when I was deciding on which college to attend, I sought the opinions of my parents, teachers, and friends. Each of them provided a unique perspective based on their own experiences and knowledge. My parents focused on the financial aspect and the reputation of the college, my teachers emphasized the quality of education and opportunities for research, while my friends considered the social life and extracurricular activities. By considering all these different viewpoints, I was able to make a well-rounded decision that I was confident in. Moreover, seeking multiple opinions can also help to validate our own thoughts and feelings. It can provide reassurance that we are on the right track or highlight areas where we might be misguided. For example, when I was contemplating a career change, I discussed it with several people in my network. Their feedback helped me to confirm that my reasons for wanting to change were valid and that I was not just acting on a whim. It also helped me to identify potential challenges and prepare for them. However, while seeking multiple opinions is beneficial, it is also important to remember that the final decision lies with us. We should not let others' opinions overshadow our own judgment and intuition. After all, we are the ones who will have to live with the consequences of our decisions. Therefore, we should use others' opinions as a guide, but not let them dictate our choices. In conclusion, seeking multiple opinions when making a tough decision is crucial. It provides different perspectives, validates our own thoughts, and helps us to make a more informed and confident decision. However, we should also trust our own judgment and make the decision that feels right for us. As the saying goes, "Two heads are better than one." But in the end, it's your head that counts.

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The paper proposes an alternative to conditional max. log likelihood for training discriminative classifiers. The argument is that the conditional log. likelihood is an upper bound to the classification error, but it overemphasizes """ the influence of incorrectly classified samples far from the decision boundary, which can lead to poor generalization. The authors propose to update the upper bound during the training process, which leads to improved classification rates. """ In addition, the authors show how to link the performance of a classifier to that of a larger system, which allows the seamless introduction """ of external constraints. """

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Why should you join the Seagoing Cowboys program? As an experienced essay writer, I can confidently say that this program offers numerous benefits for those who participate. First and foremost, the Seagoing Cowboys program has a rich history dating back to the 1940s. During World War II, the program was established to transport livestock from the United States to war-torn Europe. Since then, the program has evolved into a unique opportunity for young adults to travel the world, learn about different cultures, and gain valuable life experiences. One of the most significant benefits of joining the Seagoing Cowboys program is the opportunity for adventure. As a Seagoing Cowboy, you will have the chance to travel to different countries and experience new cultures firsthand. You will also have the opportunity to see some of the most beautiful and remote parts of the world, such as the fjords of Norway or the mountains of New Zealand. Another benefit of the Seagoing Cowboys program is the opportunity for personal growth. As a Seagoing Cowboy, you will be challenged to step outside of your comfort zone and take on new responsibilities. You will learn how to navigate unfamiliar waters, care for animals in different environments, and work as part of a team. These skills will serve you well long after your time as a Seagoing Cowboy is over. In addition to the opportunities for adventure and personal growth, the Seagoing Cowboys program also offers a unique sense of community. As a Seagoing Cowboy, you will be part of a tight-knit group of individuals who share a common goal: to transport livestock around the world. You will form lasting friendships with your fellow cowboys and develop a sense of camaraderie that is hard to find elsewhere. Finally, the Seagoing Cowboys program is an excellent way to gain practical experience in the agricultural industry. As a Seagoing Cowboy, you will have the opportunity to learn about different breeds of livestock, how to care for them, and how to navigate the complex logistics of international trade. This knowledge will be invaluable if you decide to pursue a career in agriculture or a related field. In conclusion, the Seagoing Cowboys program offers numerous benefits for those who participate. From the opportunity for adventure and personal growth to the sense of community and practical experience, this program is an excellent choice for anyone looking to challenge themselves and explore the world. So why should you join the Seagoing Cowboys program? The answer is simple: it offers a once-in-a-lifetime experience that will shape your future and broaden your horizons.

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This paper proposes a novel method for accelerating optimization near saddle points. The basic idea is to repel the current parameter vector from a running average of recent parameter values. This method is shown to optimize faster than a variety of other methods in a variety of datasets and architectures. On the surface, the proposed method seems extremely close to momentum. It would be very valuable to think of a clear diagram illustrating how it differs from momentum and why it might be better near a saddle point. The illustration of better convergence on the toy saddle example is not what I mean here—optimization speed is one thing, but another important factor is whether or not you get stuck oscillating around the saddle before converging to the global minimum (or maximum). The authors claim that their method does not use any second-order information, which could make it more widely applicable. However, this also makes me wonder if there are certain types of problems where second-order information might actually be useful. For instance, some papers have argued that Hessian information can help avoid getting stuck in poor local minima when optimizing deep neural networks. How does the author's approach handle such cases?

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Fighting, drama, bullies, and irritated teachers are all factors of attending public school. We live in a society where social anxiety and depression are now a common problem. Everyday, somewhere in the world, a child is diagnosed with anxiety. Attending a public school can worsen the symptoms of the mental illness. Anxiety can have major effects on your body including; panic attacks, depressive spells, anger outburst, crying episodes, and nausea. Drama and fighting can be an easy trigger for a student with the mental illness, and for an unknown reason, public schools have a lot of drama. Most drama comes from bullying, the popular girls give the smart girls a hard time. The typical teenage "movie". Bullying is a serious matter, we lose at least 10 children a day due to bullying. When someone is mean to you over and over again, you begin to believe that it is your fault, which can cause negative thoughts in a young persons mind. The suicide rate in the past 20 years is higher than ever. Most of the recorded suicides have to deal with bullying Doing classes from home sounds like a positive way to keep physical bullying at a minimum. I understand waking up early, not getting enough sleep, and being forced to eat the school lunches may sound like a nightmare to some, but is it really worth risking your child's mental health? Homeschooling is an alternative to attending public school that has been gaining popularity in recent years. While some argue that homeschooling can provide a more personalized and effective education, others claim that it can have negative effects on a child's social development and academic success. In this essay, I will discuss the potential benefits and drawbacks of homeschooling, particularly in regards to its impact on mental health and academic success. One of the main benefits of homeschooling is the ability to provide a personalized education tailored to the individual child's needs and learning style. Traditional public schools often have large class sizes, which can make it difficult for teachers to give each student the attention they need. Homeschooling allows parents to focus on their child's specific strengths and weaknesses, providing them with a more effective and efficient education. Additionally, homeschooling can provide a safer and more comfortable learning environment for children who may have experienced bullying or other negative experiences in traditional schools. However, there are also potential drawbacks to homeschooling. One of the main concerns is the lack of social interaction that homeschooling can provide. Children who are homeschooled may not have the same opportunities to interact with their peers, which can negatively impact their social development and ability to form relationships. Additionally, homeschooling can be isolating for both the child and the parent, which can lead to feelings of loneliness and disconnection from the community. Another potential drawback of homeschooling is the potential for biased or incomplete education. Some parents may use homeschooling as a way to indoctrinate their children with their own beliefs and values, rather than providing a well-rounded education. Additionally, homeschooling parents may not have the same level of expertise or resources as trained teachers, which can result in a less comprehensive education for the child. In terms of mental health, homeschooling can have both positive and negative effects. On the one hand, homeschooling can provide a safe and comfortable environment for children who may have experienced bullying or other negative experiences in traditional schools. This can help to reduce stress and anxiety, and promote better mental health. On the other hand, homeschooling can also limit children's opportunities for social interaction, which is essential for building healthy relationships and developing emotional intelligence. In conclusion, while homeschooling can provide some benefits in terms of personalized education and safety, it is important to consider the potential drawbacks in terms of social development and academic success. It is crucial for parents to carefully weigh the pros and cons of homeschooling and consider what is best for their child's individual needs. Additionally, it is important for society as a whole to recognize the importance of mental health and work to create safe and supportive environments for all children, whether they attend traditional schools or are homeschooled.

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Students should be in at least one or more extracurricular activities they can meet new people and make new friends find out they are actually really good at the thing they are doing they can learn new things about something that they never knew about; For example, when I started doing orchestra I made lots of new friends and I met people that I didn't even knew went to my school; also that were in some of my other classes. When I moved up to other orchestras I made more and more friends. I am really glad I joined, because then I wouldn't have met all those people and my friends. Also you might find someone who has the same interests as you do and you can maybe be try and be their friend If you join a activity and you really like it maybe you can make a career out of it. Isn't that how most famous sports players became so good at the sport and make lots of money. Please complete the essay by addressing the following points: * Discuss the benefits of participating in extracurricular activities for students, including how it can help them make new friends, discover hidden talents, and learn new skills. * Provide personal examples and research to support your argument. * Address potential counterarguments and refute them with evidence. * Conclude by summarizing the main points and reiterating the importance of extracurricular activities for students. Note: Please ensure that the essay is well-structured, coherent, and free of grammatical errors.

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The paper uses the notion of separation rank from tensor algebra to analyze the correlations induced through convolution and pooling layers in deep neural networks (DNNs). This provides an interesting perspective on the role of these layers in shaping the hypothesis space of DNNs. However, there are some aspects of the paper that could be improved upon. Firstly, the abstract does not adequately convey the main contributions of the paper. While it mentions the importance of understanding the inductive bias of DNNs, it does not highlight the specific insights provided by the authors regarding the role of pooling geometry in controlling this bias. Additionally, the abstract could benefit from providing more context about the significance of the results, such as potential applications or implications for future research.

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This work extends variational autoencoders to adapt to a new dataset containing a small number of examples. While this work is promising, two of the reviewers had serious concerns about clarity.viewer #1 found the abstract hard to understand due to excessive use of buzzwords like "meta-learning" and "matching networks". It would benefit the authors to provide clear explanations of these terms early in the manuscript. Additionally, reviewer #2 felt that the connection between the proposed method and previous works (e.g., VAEs) could have been made clearer. For example, how does the proposed approach differ from simply fine-tuning a pre-trained VAE? Overall, both reviewers agreed that further explanation and justification of the proposed method's novelty is necessary before publication.

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Many schools offer a variety of extracurricular activities that students can participate in. Although many students are recognized for all of the activities they do, some of their fellow students do not participate in any. There are many excuses children and teens come up with as to why they can't participate, but there are a couple logical reasons that support their case. If a principal made every student engage in an extracurricular activity, I would disagree. I do not think students should be forced to do certain activities they may not have the money to participate, they may be shy and performing in a sport or club is out of their comfort zone, they may not have enough time. If you are involved in sports there are many expenses that come with the honor. You have to buy the right shoes and clothes to practice in, but that's not all..You also have to pay for the uniform, the equipment, and sometimes even the transportation. For families who are financially struggling, these costs can be a significant burden. It's not fair to force these students to participate in activities that they simply cannot afford. Moreover, not all students are outgoing and comfortable in social situations. Forcing a shy or introverted student to participate in a group activity can cause unnecessary stress and anxiety. Schools should be a place where students feel safe and comfortable, not a place where they are forced to do things that make them uncomfortable. In addition, many students have other responsibilities outside of school. Some students have jobs to help support their families, while others have to take care of younger siblings or elderly relatives. Forcing these students to participate in extracurricular activities would take away from the time they need to fulfill these responsibilities. Furthermore, mandatory participation in extracurricular activities could lead to a decrease in academic performance. Students who are forced to participate in activities they are not interested in may not have enough time to focus on their studies. This could lead to lower grades and a decrease in overall academic performance. However, it's important to note that extracurricular activities do have many benefits. They can help students develop important skills such as teamwork, leadership, and time management. They can also provide students with opportunities to explore their interests and passions. Therefore, while I do not believe that participation in extracurricular activities should be mandatory, I do believe that schools should encourage students to participate in these activities. Schools could do this by offering a wide variety of activities that cater to different interests and abilities. They could also provide financial assistance to students who cannot afford to participate in certain activities. In conclusion, while extracurricular activities can provide many benefits, participation in these activities should not be mandatory. Students should have the freedom to choose whether or not they want to participate in these activities. Schools should provide support and encouragement, but ultimately, the decision should be left up to the students.

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The authors present an interesting approach to transferring skills using reinforcement learning. By training invariant feature spaces, they enable the transfer of knowledge between morphologically different agents. The abstract provides a clear overview of the paper's contributions, and the introduction effectively sets up the problem and motivates the need for transfer learning. However, I would suggest adding more detail on the specific techniques used to learn the invariant feature spaces, such as the type of neural network architecture employed or any regularization methods used to enforce invariance. Additionally, it might be helpful to provide more examples of real-world applications where this technique could be useful, beyond just simulated robotic manipulation skills.

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Most high school students struggle with waking up on time or having a good nights rest. This often will cause a lack of focus and a drop in their academic success. There should be other options for students to still get their education without having to actually go to school. Having a way to work from home eliminate those factors and could possibly better students in their schoolwork. Therefor, students would benefit from distance learning it will help those in need of a flexed schedule, those with social anxiety, for those with parents who simple believe in home schooling. There are some people that can't be in a crowed of people because it causes them to get anxious. There are also people with other types of disorders that may lead them to need to be in the hospitals or just unable to function well with other people. People who are in situations like this can benefit from being able to take online classes because it can take away the discomfort of being around a large group of people. I have a friend who has social anxiety, and it cause her to have panic attacks when she even sees a lot of people all at once. She had to drop out of high school because she couldn't deal with the stress of being in a classroom full of kids her age. If she would have had the option to do online classes she would have been able to graduate with the rest of her class instead of having to wait until she was old enough to take the GED test. Distance learning can also benefit students who have other daily obligations, such as work or caring for siblings. For example, a student who works part-time to support their family may not be able to attend traditional school due to their work schedule. With distance learning, they can complete their coursework on their own time and still fulfill their work obligations. Similarly, a student who is responsible for caring for younger siblings may not be able to attend school due to their family responsibilities. Distance learning allows them to balance their family obligations while still pursuing their education. Personally, I believe that distance learning is a great option for high school students who struggle with waking up on time or having a good night's rest. It provides them with the flexibility to complete their coursework on their own time and at their own pace. Additionally, it eliminates the stress of being in a traditional classroom setting, which can be overwhelming for some students. Overall, distance learning can be a valuable tool for students who need a more flexible approach to education.

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The authors explore the idea of deep-learning a static analyzer. They do it with a toy programming language and a very simplified analysis problem -- just checking if all variables are initalized. While the idea is interesting and might be developped into a tool in the future, the toy task presented in this paper is too simple to warrant an ICLR submission.ropriate for a workshop like NeurIPS/ICML. Furthermore, while the authors claim that their approach outperforms previous ones (based on feature engineering, HMMs, or RNNs), they don't provide any quantitative comparison between these methods and their own. This makes it difficult to assess the effectiveness of their proposed method.

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Driverless cars would be good in the future because nobody would have to spend money on cars when they can actually buy other thing that they need. The driverless cars would use half fuel today's taxis need. Which is going to make people save money. The driverless car would offer far more flexibility than a bus. The driverless cars could change the world in a positive way. Google has had cars that could drive independently under specific conditions since 2009. Their cars have driven more than half million miles without crashing. People would be so curious about the cars that they would love to try them. People is getting to discover more and more technology which is fascinating stuff and a driverless car would be a surprise for them and they would like to know about it. A driverless car is something that television and movies have long been fascinated with cars that could drive themselves. A driverless car could be a benefit for a lot of people. People been trying to make a driverless car for a long time. Please complete the essay with at least 318 words.

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I believe for a student to have a B average should be able to participate in school sports or in other school activities is a reasonable policy. It is privilege to be able to participate in school sports and activities cause you learn more things that are useful in the future and help you gain physical strength on some levels You also should keep this as a school policy because you shouldn't be able to participate if you are either failing a class or struggling to pass that class you need you won't be able to have any ability to do so in school sports and all..However, this policy also has its drawbacks. It may discourage students who are not academically inclined but excel in sports or other activities. These students may feel marginalized and lose their motivation to participate in school activities. Furthermore, it may create an unhealthy competition among students to maintain their grades, leading to stress and anxiety. In my opinion, while maintaining a B average should be encouraged, it should not be a prerequisite for participating in school sports or activities. Education is not just about academics; it is also about holistic development. Sports and other activities play a crucial role in developing a student's personality, leadership skills, and team spirit. They also provide a much-needed break from academics and help in reducing stress. Instead of making a B average a requirement, schools should provide additional academic support to students who are struggling. This way, they can improve their grades without missing out on the benefits of participating in school activities. Schools should strive to create an environment where every student, regardless of their academic performance, can explore their interests and talents. This approach will not only improve the students' self-esteem but also make learning more enjoyable for them.

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Luke has a certain point of view on convincing others to participate in this program Luke has this point of view because he thinks the trips were fun on board and he feels it opened up the world to him. It also made him more aware of the other peoples needs in other countries. The awarness stayed with him and thats why he feels this way about other people participating i the program. In the story it states that "Luke also found time to have fun on board." I think this helps his point of view because he is convincing others to go on this fun trip. The text also states that these fun activities or games helped them pass time. Luke's point of view might of changed by having fun and thats a part of why he is convincing others to participate. In the text Luke said "It opened the world to him." The text states that he was grateful for the oppotunity.

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I am aware that you are aware that some kids from our school are debating wether to do community service or not. I would highly encourage you to help these boys see the benefit in doing this. I know for a fact that I am doing this to better our community and our school. I don't recommend that you should require kids to do this I'm just saying that you should encourage them to do so. Community service is a great way for students in 7th to 12th grade to give back to their community and make a positive impact on the world around them. Through community service, students can learn valuable skills such as teamwork, leadership, and problem-solving, while also making a difference in the lives of others. Personally, I have participated in several community service projects, including volunteering at a local food bank and cleaning up a nearby park. These experiences have taught me the importance of giving back to my community and have shown me the positive impact that I can have on the world. I believe that all students should have the opportunity to participate in community service, as it can help them develop important life skills and a sense of social responsibility. Schools and communities can encourage more students to get involved in community service by providing opportunities for students to volunteer and by promoting the benefits of community service. For example, schools can offer community service hours as part of their curriculum, or they can organize community service events and invite students to participate. Communities can also provide resources and support for students who want to organize their own community service projects. In conclusion, community service is a valuable experience for students in 7th to 12th grade. It provides an opportunity for students to give back to their community, develop important life skills, and make a positive impact on the world. I highly encourage all students to get involved in community service and to take advantage of the many benefits it has to offer.

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This paper is an extension of Lenc&Vedaldi15 paper, showing CNN representations at FC7 layer are to certain extent equivariant to various classes of transformations and that training with a certain group of transformation makes the representations more equivalent. Authors performed a large amount of experiments, training over 30 networks with different forms of jitter, which is quite impressive. However it is rather difficult to find a main message of this work. Yes, authors measured the properties on a different layer than the Lenc&Vedaldi15, however it is hard to find some novel insights other than the known fact that jitter helps to achieve invariance. The evaluation seems to be mostly correct, however the paper does not seem to be solving the task advertised in its title really well. Major issues are in the experiments with the representation distances: * The selection of only FC7 is a bit controversial - it is followed only by a single classification layer (FC8) to the common output - class likelyhoods. Because the FC8 is just a linear projections, what the equivalence map does is just to re-project the FC8 weights of the attached network to the weights of the original network. Probably performing similar experiments but on more layers may be more useful (as the networks are already trained). * The experiment with representation distance is missing what is the classification error on the testing dataset. It would have been interesting to see if there was any correlation between the representation distance and the classification performance. * There is no discussion about the computational cost of the proposed method for improving the equivariance property of the neural network. Positive aspects of the paper include: * Good experimental design and execution. * Interesting results regarding the invariance and equivariance properties of convolutional neural networks.

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Summary: The paper presents an approach – Neural Answer Construction Model for the task of answering non-factoid questions, in particular, love-advice questions. The two main features of the proposed model are the following – 1) it incorporates the biases of semantics behind questions into word embeddings, 2) in addition to optimizing for closeness between questions and answers, it also optimizes for optimum combination of sentences in the predicted answer. The proposed model is evaluated using the dataset from a Japanese online QA service and is shown to outperform the baseline model (Tan et al. 2015) by 20% relatively (6% absolutely). The paper also experiments with few other baseline models (ablations of the proposed model). Strengths: 1. The two motivations behind the proposed approach – need to understand the ambiguous use of words depending on context, and need to generate new answers rather than just selecting from answers held by QA sites """ 2. The proposed model is able to generate answers that are more appropriate for the context of the question. """ Conclusion: """ 3. The proposed model is practical and has been applied to the love-advice service in Oshietegoo. """

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