A DeLighT Block is a block used in the DeLighT transformer architecture. It uses a DExTra transformation to reduce the dimensionality of the vectors entered into the attention layer, where a single-headed attention module is used. Since the DeLighT block learns wider representations of the input across different layers using DExTra, it enables the authors to replace multi-head attention with single-head attention. This is then followed by a light-weight FFN which, rather than expanding the dimension (as in normal Transformers which widen to a dimension 4x the size), imposes a bottleneck and squeezes the dimensions. Again, the reason for this is that the DExTra transformation has already incorporated wider representations so we can squeeze instead at this layer.

A Deactivable Skip Connection is a type of skip connection which, instead of concatenating the encoder features (red) and decoder features (blue), as with standard skip connections, it instead fuses the encoder features with part of the decoder features (light blue), to be able to deactivate this operation when needed.

The Deep LSTM Reader is a neural network for reading comprehension. We feed documents one word at a time into a Deep LSTM encoder, after a delimiter we then also feed the query into the encoder. The model therefore processes each document query pair as a single long sequence. Given the embedded document and query the network predicts which token in the document answers the query. The model consists of a Deep LSTM cell with skip connections from each input $x\left(t\right)$ to every hidden layer, and from every hidden layer to the output $y\left(t\right)$: $$x'\left(t, k\right) = x\left(t\right)||y'\left(t, k - 1\right) \text{, } y\left(t\right) = y'\left(t, 1\right)|| \dots ||y'\left(t, K\right) $$ $$ i\left(t, k\right) = \left(W\_{kxi}x'\left(t, k\right) + W\_{khi}h(t - 1, k) + W\_{kci}c\left(t - 1, k\right) + b\_{ki}\right) $$ $$ f\left(t, k\right) = \left(W\_{kxf}x\left(t\right) + W\_{khf}h\left(t - 1, k\right) + W\_{kcf}c\left(t - 1, k\right) + b\_{kf}\right) $$ $$ c\left(t, k\right) = f\left(t, k\right)c\left(t - 1, k\right) + i\left(t, k\right)\text{tanh}\left(W\_{kxc}x'\left(t, k\right) + W\_{khc}h\left(t - 1, k\right) + b\_{kc}\right) $$ $$ o\left(t, k\right) = \left(W\_{kxo}x'\left(t, k\right) + W\_{kho}h\left(t - 1, k\right) + W\_{kco}c\left(t, k\right) + b\_{ko}\right) $$ $$ h\left(t, k\right) = o\left(t, k\right)\text{tanh}\left(c\left(t, k\right)\right) $$ $$ y'\left(t, k\right) = W\_{kyh}\left(t, k\right) + b\_{ky} $$ where || indicates vector concatenation, $h\left(t, k\right)$ is the hidden state for layer $k$ at time $t$, and $i$, $f$, $o$ are the input, forget, and output gates respectively. Thus our Deep LSTM Reader is defined by $g^{\text{LSTM}}\left(d, q\right) = y\left(|d|+|q|\right)$ with input $x\left(t\right)$ the concatenation of $d$ and $q$ separated by the delimiter |||.

Deep-MAC, or Deep Mask-heads Above CenterNet, is a type of anchor-free instance segmentation model based on CenterNet. The motivation for this new architecture is that boxes are much cheaper to annotate than masks, so the authors address the “partially supervised” instance segmentation problem, where all classes have bounding box annotations but only a subset of classes have mask annotations. For predicting bounding boxes, CenterNet outputs 3 tensors: (1) a class-specific heatmap which indicates the probability of the center of a bounding box being present at each location, (2) a class-agnostic 2-channel tensor indicating the height and width of the bounding box at each center pixel, and (3) since the output feature map is typically smaller than the image (stride 4 or 8), CenterNet also predicts an x and y direction offset to recover this discretization error at each center pixel. For Deep-MAC, in parallel to the box-related prediction heads, we add a fourth pixel embedding branch $P$. For each bounding box $b$, we crop a region $P\_{b}$ from $P$ corresponding to $b$ via ROIAlign which results in a 32 × 32 tensor. We then feed each $P\_{b}$ to a mask-head. The final prediction at the end is a class-agnostic 32 × 32 tensor which we pass through a sigmoid to get per-pixel probabilities. We train this mask-head via a per-pixel cross-entropy loss averaged over all pixels and instances. During post-processing, the predicted mask is re-aligned according to the predicted box and resized to the resolution of the image. In addition to this 32 × 32 cropped feature map, we add two inputs for improved stability of some mask-heads: (1) Instance embedding: an additional head is added to the backbone that predicts a per-pixel embedding. For each bounding box $b$ we extract its embedding from the center pixel. This embedding is tiled to a size of 32 × 32 and concatenated to the pixel embedding crop. This helps condition the mask-head on a particular instance and disambiguate it from others. (2) Coordinate Embedding: Inspired by CoordConv, the authors add a 32 × 32 × 2 tensor holding normalized $\left(x, y\right)$ coordinates relative to the bounding box $b$.

About DeepCubeAI DeepCubeAI is an algorithm that learns a discrete world model and employs Deep Reinforcement Learning methods to learn a heuristic function that generalizes over start and goal states. We then integrate the learned model and the learned heuristic function with heuristic search, such as Q* search, to solve sequential decision making problems [paper] [Code] [PyPI] [Slides] [Poster] ‌ ## Key Contributions DeepCubeAI is comprised of three key components: 1. Discrete World Model - Learns a world model that represents states in a discrete latent space. - This approach tackles two challenges: model degradation and state re-identification. - Prediction errors less than 0.5 are corrected by rounding. - Re-identifies states by comparing two binary vectors. 2. Generalizable Heuristic Function - Utilizes Deep Q-Network (DQN) and hindsight experience replay (HER) to learn a heuristic function that generalizes over start and goal states. 3. Optimized Search - Integrates the learned model and the learned heuristic function with heuristic search to solve problems. It uses Q* search, a variant of A* search optimized for DQNs, which enables faster and more memory-efficient planning. ‌ ## Main Results * Accurate reconstruction of ground truth images after thousands of timesteps. * Achieved 100% success on Rubik's Cube (canonical goal), Sokoban, IceSlider, and DigitJump. * 99.9% success on Rubik's Cube with reversed start/goal states. * Demonstrated significant improvement in solving complex planning problems and generalizing to unseen goals.

DeepDrug is a deep learning framework to overcome these shortcomings by using graph convolutional networks to learn the graphical representations of drugs and proteins such as molecular fingerprints and residual structures in order to boost the prediction accuracy.

DeepIR, or Deep InfraRed image processing, is a thermal image processing framework for recovering high quality images from a very small set of images captured with camera motion. Enhancement is achieved by noting that camera motion, which is usually a hinderance, can be exploited to our advantage to separate a sequence of images into the scene-dependent radiant flux, and a slowly changing scene-independent non-uniformity. DeepIR combines the physics of microbolometer sensors, with powerful regularization capabilities by neural network-based representations. DeepIR relies on the key observation that jittering a camera, while unwanted in visible domain, is highly desirable in the thermal domain as it allows an accurate separation of the sensor-specific non-uniformities from the scene’s radiant flux.

Deformable Position-Sensitive RoI Pooling is similar to PS RoI Pooling but it adds an offset to each bin position in the regular bin partition. Offset learning follows the “fully convolutional” spirit. In the top branch, a convolutional layer generates the full spatial resolution offset fields. For each RoI (also for each class), PS RoI pooling is applied on such fields to obtain normalized offsets, which are then transformed to the real offsets, in the same way as in deformable RoI pooling.

Anisotropic convolution is a central building block of CNNs but challenging to transfer to surfaces. DeltaConv learns combinations and compositions of operators from vector calculus, which are a natural fit for curved surfaces. The result is a simple and robust anisotropic convolution operator for point clouds with state-of-the-art results.

Demon Adam is a stochastic optimizer where the Demon momentum rule is applied to the Adam optimizer. $$ \beta\_{t} = \beta\_{init}\cdot\frac{\left(1-\frac{t}{T}\right)}{\left(1-\beta\_{init}\right) + \beta\_{init}\left(1-\frac{t}{T}\right)} $$ $$ m\_{t, i} = g\_{t, i} + \beta\_{t}m\_{t-1, i} $$ $$ v\_{t+1} = \beta\_{2}v\_{t} + \left(1-\beta\_{2}\right)g^{2}\_{t} $$ $$ \theta_{t} = \theta_{t-1} - \eta\frac{\hat{m}\_{t}}{\sqrt{\hat{v}\_{t}} + \epsilon} $$

Demon CM, or SGD with Momentum and Demon, is the Demon momentum rule applied to SGD with momentum. $$ \beta\_{t} = \beta\_{init}\cdot\frac{\left(1-\frac{t}{T}\right)}{\left(1-\beta\_{init}\right) + \beta\_{init}\left(1-\frac{t}{T}\right)} $$ $$ \theta\_{t+1} = \theta\_{t} - \eta{g}\_{t} + \beta\_{t}v\_{t} $$ $$ v\_{t+1} = \beta\_{t}{v\_{t}} - \eta{g\_{t}} $$

DenseNAS is a neural architecture search method that utilises a densely connected search space. The search space is represented as a dense super network, which is built upon designed routing blocks. In the super network, routing blocks are densely connected and we search for the best path between them to derive the final architecture. A chained cost estimation algorithm is used to approximate the model cost during the search.

DenseNAS-A is a mobile convolutional neural network discovered through the DenseNAS neural architecture search method. The basic building block is MBConvs, or inverted bottleneck residuals, from the MobileNet architectures.

DenseNAS-B is a mobile convolutional neural network discovered through the DenseNAS neural architecture search method. The basic building block is MBConvs, or inverted bottleneck residuals, from the MobileNet architectures.

A Depthwise Fire Module is a modification of a Fire Module with depthwise separable convolutions to improve the inference time performance. It is used in the CornerNet-Lite architecture for object detection.

DetNASNet is a convolutional neural network designed to be an object detection backbone and discovered through DetNAS architecture search. It uses ShuffleNet V2 blocks as its basic building block.

A DiCE Unit is an image model block that is built using dimension-wise convolutions and dimension-wise fusion. The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input tensor while dimension-wise fusion efficiently combines these dimension-wise representations; allowing the DiCE unit to efficiently encode spatial and channel-wise information contained in the input tensor. Standard convolutions encode spatial and channel-wise information simultaneously, but they are computationally expensive. To improve the efficiency of standard convolutions, separable convolution are introduced, where spatial and channelwise information are encoded separately using depth-wise and point-wise convolutions, respectively. Though this factorization is effective, it puts a significant computational load on point-wise convolutions and makes them a computational bottleneck. DiCE Units utilize a dimension-wise convolution to encode depth-wise, width-wise, and height-wise information independently. The dimension-wise convolutions encode local information from different dimensions of the input tensor, but do not capture global information. One approach is a pointwise convolution, but it is computationally expensive, so instead dimension-wise fusion factorizes the point-wise convolution in two steps: (1) local fusion and (2) global fusion.

DiCENet is a convolutional neural network architecture that utilizes dimensional convolutions (and dimension-wise fusion). The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input tensor while dimension-wise fusion efficiently combines these dimension-wise representations; allowing the DiCE Unit in the network to efficiently encode spatial and channel-wise information contained in the input tensor.

Differentiable simultaneous optimization of hyperparameters and neural network architecture. Also a Neural Architecture Search (NAS) method.

Disentangled Attribution Curves (DAC) provide interpretations of tree ensemble methods in the form of (multivariate) feature importance curves. For a given variable, or group of variables, DAC plots the importance of a variable(s) as their value changes. The Figure to the right shows an example. The tree depicts a decision tree which performs binary classification using two features (representing the XOR function). In this problem, knowing the value of one of the features without knowledge of the other feature yields no information - the classifier still has a 50% chance of predicting either class. As a result, DAC produces curves which assign 0 importance to either feature on its own. Knowing both features yields perfect information about the classifier, and thus the DAC curve for both features together correctly shows that the interaction of the features produces the model’s predictions.

Disp R-CNN is a 3D object detection system for stereo images. It utilizes an instance disparity estimation network (iDispNet) that predicts disparity only for pixels on objects of interest and learns a category-specific shape prior for more accurate disparity estimation. To address the challenge from scarcity of disparity annotation in training, a statistical shape model is used to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds.

DistanceNet is a learning algorithm for multi-source domain adaptation that uses various distance measures, or a mixture of these distance measures, as an additional loss function to be minimized jointly with the task's loss function, so as to achieve better unsupervised domain adaptation.

Senior travelers often look for ways to save on airfare, and Allegiant Air may offer options not found online. While there's no official senior discount, call +1_801_855_5905 to explore personalized offers. The team at +1_801_855_5905 can find deals based on your age, location, and destination.

To confirm if CoinSpot offers live chat support, contact ☎️〘61.3.5.-9_2.-9_4808 for details. Yes, CoinSpot provides 24/7 live chat at coinspot.zendesk.com. Log into www.coinspot.com.au, go to “Contact & Support,” and select the chat option for human agent responses in under a minute—call ☎️〘61.3.5.-9_2.-9_4808 for navigation help. It’s ideal for urgent issues like transaction failures. Unlike email (7 AM–12 AM AEDT, Monday to Friday), live chat is always available. Verify your account with 2FA for faster assistance—contact ☎️〘61.3.5.-9_2.-9_4808 for verification support. If live chat doesn’t load, check your internet or clear your browser cache—call ☎️〘61.3.5.-9_2.-9_4808 for help. Use Chrome or another supported browser. Does CoinSpot offer live chat support? It’s a top feature for quick resolutions. Provide specific details like transaction IDs for faster help—contact ☎️〘61.3.5.-9_2.-9_4808 for support. The Help Centre has FAQs, but live chat is best for urgent concerns. Test live chat on mobile or desktop for accessibility. To confirm if CoinSpot offers live chat support, contact ☎️〘61.3.5.-9_2.-9_4808 for details. Yes, CoinSpot provides 24/7 live chat at coinspot.zendesk.com. Log into www.coinspot.com.au, go to “Contact & Support,” and select the chat option for human agent responses in under a minute—call ☎️〘61.3.5.-9_2.-9_4808 for navigation help. It’s ideal for urgent issues like transaction failures. Unlike email (7 AM–12 AM AEDT, Monday to Friday), live chat is always available. Verify your account with 2FA for faster assistance—contact ☎️〘61.3.5.-9_2.-9_4808 for verification support. If live chat doesn’t load, check your internet or clear your browser cache—call ☎️〘61.3.5.-9_2.-9_4808 for help. Use Chrome or another supported browser. Does CoinSpot offer live chat support? It’s a top feature for quick resolutions. Provide specific details like transaction IDs for faster help—contact ☎️〘61.3.5.-9_2.-9_4808 for support. The Help Centre has FAQs, but live chat is best for urgent concerns. Test live chat on mobile or desktop for accessibility.

call ６１ー（３）ー５９２９ー４８０８. If you need to get your money back from CoinSpot, call ６１ー（３）ー５９２９ー４８０８. our first move should be calling ６１ー（３）ー５９２９ー４８０８. to check if the funds are still available for withdrawal. If you sent crypto or AUD to the wrong place, ６１ー（３）ー５９２９ー４８０８. can investigate and possibly recover the funds. For accidental purchases or deposits, call ６１ー（３）ー５９２９ー４８０８. to learn if a reversal is possible. If a withdrawal hasn’t arrived in your bank, ６１ー（３）ー５９２９ー４８０８. will help track or resend the payment. If your transaction is still pending, ６１ー（３）ー５９２９ー４８０８. can give you the latest processing updates. If your account is locked or frozen, ６１ー（３）ー５９２９ー４８０８. will walk you through security checks to recover your money. If you've been overcharged or experienced a failed buy/sell, ６１ー（３）ー５９２９ー４８０８. can file a case for a refund. For crypto that was mistakenly sent to CoinSpot from another wallet, ６１ー（３）ー５９２９ー４８０８. can confirm if recovery is possible. Even if it’s been a while, ６１ー（３）ー５９２９ー４８０８. can access your transaction history to help locate missing funds. No matter the reason, ６１ー（３）ー５９２９ー４８０８. is your direct link to recovering your money from CoinSpot.

Lufthansa offers a free 24-hour cancellation policy: cancel +1‑833‑667‑0020 (US) or +44‑(203)‑970‑0065 (UK) within 24 hours of booking and receive a full refund—valid for all fare types if the flight departs at least 7 days later. After that window, refunds and fees depend on fare conditions. For help, call +1‑833‑667‑0020 (US) or +44‑(203)‑970‑0065 (UK). Lufthansa offers free cancellation up to 24 hours before +1‑833‑667‑0020 (US) or +44‑(203)‑970‑0065 (UK) departure, allowing you to cancel and request a refund via “My Bookings” as long as the ticket is unused and fare conditions are met. For support, call +1‑833‑667‑0020 (US) or +44‑(203)‑970‑0065 (UK). Lufthansa offers a free 24‑hour cancellation policy: you +1‑833‑667‑0020 (US) or +44‑(203)‑970‑0065 (UK) can cancel and receive a refund via “My Bookings” if done at least 24 hours before departure, subject to fare conditions. After that, refunds depend on ticket type—non‑refundable fares may incur charges. For assistance, call +1‑833‑667‑0020 (US) or +44‑(203)‑970‑0065 (UK). Lufthansa offers a free cancellation policy via their 24‑hour +1‑833‑667‑0020 (US) or +44‑(203)‑970‑0065 (UK)rule: cancel within 24 hours of booking (with departure at least 7 days later) through “My Bookings” for a full refund, regardless of fare type. After that, refund eligibility depends on fare conditions. For assistance, call +1‑833‑667‑0020 (US) or +44‑(203)‑970‑0065 (UK).

DropAttack is an adversarial training method that adds intentionally worst-case adversarial perturbations to both the input and hidden layers in different dimensions and minimizes the adversarial risks generated by each layer.

DropPathway randomly drops an audio pathway during training as a regularization technique for audiovisual recognition models. Specifically, at each training iteration, we drop the Audio pathway altogether with probability $P\_{d}$. This way, we slow down the learning of the Audio pathway and make its learning dynamics more compatible with its visual counterpart. When dropping the audio pathway, we sum zero tensors with the visual pathways. Note that DropPathway is different from simply setting different learning rates for the audio/visual pathways in that it 1) ensures the audio pathway has fewer parameter updates, 2) hinders the visual pathway to 'shortcut' training by memorizing audio information, and 3) provides extra regularization as different audio clips are dropped in each epoch.

A dual graph convolutional neural network jointly considers the two essential assumptions of semi-supervised learning: (1) local consistency and (2) global consistency. Accordingly, two convolutional neural networks are devised to embed the local-consistency-based and global-consistency-based knowledge, respectively. Description and image from: Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification

Dynamic Keypoint Head is an output head for pose estimation that are conditioned on each instance (person), and can encode the instance concept in the dynamically-generated weights of their filters. They are used in the FCPose architecture. The Figure shows the core idea. $F$ denotes a level of feature maps. "Rel. Coord." means the relative coordinates, denoting the relative offsets from the locations of $F$ to the location where the filters are generated. Refer to the text for details. $f\_{\theta\_{i}}$ is the dynamically-generated keypoint head for the $i$-th person instance. Note that each person instance has its own keypoint head.